Sentienta Blog

Category: Multi-agent Systems

  • AI and Ethical Dilemmas

    Every day, professionals in high-stakes fields face decisions with no clear right answer – ethical dilemmas where legal, emotional, and organizational factors collide. Consider a child protective services worker facing a borderline case of parental neglect: does intervention help or harm? Or an executive deciding whether to disclose a data breach before earnings—protect transparency or mitigate fallout? These aren’t just hard decisions, they’re decisions without a script.

    And yet, how we structure our thinking in these moments can make all the difference. Are we weighing the right factors? Do we understand the legal boundaries? Are we even asking the right questions? As emerging AI-driven frameworks begin to surface in practice, more professionals are discovering that while AI won’t decide for us, it can help us think more clearly, completely, and confidently.

    Can AI Help?

    Generative AI is already influencing how professionals think through ethical challenges. It does not make decisions, but it can structure the problem by surfacing similar case patterns, identifying key legal and ethical considerations, and mapping jurisdictional obligations.

    More advanced systems, such as multi-agent systems like Sentienta, go a step further. Each agent represents a distinct lens, such as clinical, legal, ethical, or operational, and interacts with the others to refine its perspective. This simulated dialogue mirrors the way real professionals weigh competing priorities in complex situations.

    The outcome is not a predetermined answer. It is a clearer framework for applying critical, reasoned judgment with a fuller understanding of what is at stake.

    Working Through an Example

    Consider a hospital attending urged to discharge a patient she believes isn’t ready. The ethical tension is acute: balancing care against pressure, advocacy against compliance. AI can’t make the call, but it can help hersee the dimensions more clearly.

    Using AI-guided reflection, she might explore the decision through multiple lenses:

    Patient Autonomy & Consent

    DimensionAutonomy-Relevant Considerations
    Patient Consent/ParticipationHas the patient been informed of the potential discharge plan? Do they comprehend the clinical status and associated risks of discharge now vs. staying?
    VoluntarinessIf the patient accepts discharge under pressure, are they truly consenting—or are they being steered by institutional priorities unknown to them?
    Communication TransparencyHas there been adequate, understandable disclosure to the patient about why the discharge is being considered despite clinical concerns? That includes explaining divergent opinions between clinician and administration.
    Respect for PreferencesIf the patient is capable and has a strong preference to stay, this may carry ethical weight—especially when discharge isn’t mandated by emergency triage or capacity limits.

    Clinical Risk / Patient Safety:

    Risk DomainKey Risk Elements
    Clinical DeteriorationDischarging a patient prematurely may lead to worsening of their condition, potentially resulting in readmission or adverse events.
    Continuity of Care GapsIf appropriate outpatient resources (home care, follow-up, transportation, medication access) are not securely arranged, immediate risks increase post-discharge.
    Foreseeable HarmIf harm could reasonably be anticipated due to medical instability, lack of caregiver support, or unsafe home environments, nonmaleficence is at stake.
    Moral HazardAllowing non-clinical forces to steer discharge undermines safety culture and may normalize unsafe practices.

    Legal / Regulatory Constraints:

    TensionLegal-Ethical Intersection
    Institutional Priorities vs. Individual Standard of CareLegal duty attaches primarily to the clinician’s judgment—not to administrative directives unless they provide new clinical information.
    Documentation vs. ObedienceLegally, failure to record dissent from premature discharge may increase risk, even if clinician internally disapproved. Documentation is a risk-mitigating legal tool.
    Disclosure to PatientEthical and legal norms may converge in expecting that the patient be made aware if discharge decisions are shaped by non-clinical considerations.

    Resource Allocation & Fairness:

    Allocation PrincipleApplication Challenge
    Clinical Need & BenefitPremature discharge contradicts the “maximize benefit” principle unless staying yields little added recovery—which the physician believes is not the case.
    Equity for the VulnerableIf the patient has greater need for support post-discharge (social, functional), discharging them may disproportionately harm the worst-off.
    Transparency in RationaleEthical allocation requires openness: if the patient’s discharge is based on resource need, that should be disclosed to the care team and perhaps patient.

    Professional Standards & Codes:

    DomainProfessional Responsibility
    Team CommunicationAlerting case management or ethical consultation teams when discharge disputes arise.
    Boundary IntegrityPhysicians and nurses should not allow administrative mandates to replace clinical assessment in decision-making.
    DocumentationClear recording of medical judgment and concerns about premature discharge helps uphold professional accountability and mitigate ethical risk.

    AI doesn’t offer a verdict—what it offers is structure: helping professionals expand their field of view, reduce blind spots, and clarify which competing pressures are ethical, legal, or managerial. Through that scaffolding, better decisions become possible—even in gray zones.

    Conclusion

    AI is often portrayed as a solution to humanity’s hardest problems. But its real value may lie in something more grounded: helping us navigate complexity with greater clarity. In ethics-heavy professions, where the right decision is rarely obvious, AI can act as a thinking partner, surfacing relevant perspectives, legal guardrails, and competing values.

    Rather than replacing human judgment, it challenges us to sharpen it. Could this be the real promise of AI: not as a decider, but as a catalyst for better reasoning when it matters most? In a world anxious about what AI might take from us, perhaps its greatest gift is helping us think more critically and more humanely.

  • From Values to Interfaces: How to Build with Sentienta

    Create Your Own Sentienta-Based Application

    Sentienta is a platform for building custom GenAI applications with structured, multi-agent reasoning. In this post, we walk through how to use Sentienta’s APIs to define agent roles, initiate a collaborative thinking process, and extract structured, explainable decisions.

    You’ll see how to construct a team of reasoning agents, set up their dialog cycles, and manage the reflective output—complete with code examples. For deeper context on how Recursive Reasoning supports coherence and value alignment, check out these posts (here and here). Today, the focus is practical: how to build your own deliberative AI, step by step.

    Why Build Your Own Application?

    Prebuilt AI tools often assume a single general-purpose use case. But high-friction domains like compliance oversight, clinical decision support, or ethics triage require applications tailored to specific policies, workflows, and trust requirements. Building your own application gives you control over behavior, oversight logic, and how responses are generated, reviewed, and stored.

    With Sentienta, you can specify how agents think, not just what they say. That makes it possible to design solutions that reflect your institutional values, follow internal processes, and produce outputs that stand up to audit or review. Instead of adapting your use case to fit a generic product, you shape the application to reflect how your organization reasons.

    Basic Steps to Create a Sentienta-Enabled Webapp

    Building your own Sentienta-powered web application begins with designing the agent team, then wiring a lightweight frontend to communicate with it. Here’s how to get started:

    1. Design and Test Your Agents

    Start in Sentienta’s platform by configuring the agents your application needs. You can define a team for your agents, the individual agent personas, and test team interactions. (See Tips and Tricks and Team Dynamics for background) Or just use the Quick Start to create the team for you (recommended).

    2. Build the Web Interface

    The frontend can be written in plain HTML and JavaScript – no framework required. This keeps the integration minimal and easy to deploy.

    3. Authenticate Using Your API Key

    You can implement a complete login capability in your application, and we will discuss this in more detail in a future post, but perhaps the easiest way to authenticate your webapp is to create an application-specific key from your Sentienta account.

    Visit the Agent Studio section of the documentation to create a project and generate an authentication key specific to your app. This key authorizes your web client to access your configured agent team.

    Then in your webapp’s main page insert the following code:

    Be sure to fill in the key you created and the domain of your webapp (you needed that to create a key). Then fill in your team team and the list of agents you created for this app.

    Your webapp will need to know the access endpoints that Sentienta exposes. So include these in your javascript file that will be accessing the APIs:

    Finally, to complete the initialization, get a token using your ownerKey with your first call to Sentienta:

    You will use this token in all subsequent calls to Sentienta. It authenticates your application for those API calls.

    4. Send Queries to the Agent Team

    Set up your frontend to send user inputs as POST requests to the API endpoint associated with your team. Each message is passed to your agent team for deliberation and analysis. Here is how you submit a query to your team:

    You supply the idToken for authentication, and a queryID which is just an index to assign to the dialog so when you query for the results, Sentienta will know which dialog you want. The teamName is the team that you want to query, and the agents is a string of comma-separated agent names that you assigned to the team.

    Although your Sentienta.init() call initiated with a specific list of agents and team, you can alter this at query time to limit the agents being called, or address a different team.

    You can see from this code that the query is simply fired-off, but the actual results of the query are retrieved with a call to getAgentsDialog, discussed next.

    5. Receive Responses from the Team

    The server returns structured outputs, capturing not only the final response but also insights from the team’s reasoning process. You can use this to render explanations, examine individual agent responses to a query, and orchestrate your application using the agent responses.

    Sentienta is different from most chat applications because its agents interact with each other and the team dialog is central to what it produces. In order to access this dialog, the data is retrieved iteratively, in a loop:

    The function fetchData is where the retrieveEndpoint is actually called. You supply your authentication token, the queryID that identifies the dialog and a dialogIndex that increments over the agent responses. The dialogIndex is incremented on the server-side but it passed back to the client-side for convenience.

    Response data is in response.body, and is structured strings. You can write your own handler handleSentientaResponse that will use the data in that string to decide how to display or use the responses in your application.

    The while-loop in getAgentsDialog will continue for a fixed count, but can be terminated early should an ‘EOD‘ token appear in the response.

    That’s all that is needed to get started building a webapp powered by teams of agents.

    Conclusion: Build What Your Workload Demands

    Sentienta gives you the tools to design custom agents, assemble them into reasoning teams, and deploy applications that use them effectively. Integration is simple: request an authentication token, send queries, receive structured reasoning. By building your own application, you align AI behavior with your business logic—at the scale and sensitivity your domain requires.

  • How Recursive Reasoning Gives Rise to Functional Identity—And Why It Matters

    Why Values Matter — From Evolution to Cooperation

    Humans did not evolve morals out of nobility. We evolved them because survival depends on cooperation. As individuals, we are vulnerable. As groups, we gain resilience, division of labor, and protection. For a group to function, its members must share expectations about how to act, what matters, and what can be trusted.

    These shared values do more than guide choices. They create a stable framework for interpreting behavior, resolving conflict, and predicting future actions. Without them, coordination breaks down. Even effective decisions can fracture a group if they feel arbitrary or betray prior commitments.

    Throughout human evolution, groups that upheld shared norms such as fairness, reciprocity, and loyalty proved more adaptable. Trust followed from consistency, and cohesion followed from accountability. Values, in this sense, are not abstract ideals. They are strategies for group survival.

    Why AI Needs Shared Values and Consistent Behavior

    In any organization, trust depends on consistency. When institutions or agents act in line with their stated principles, people know what to expect. This makes it easier to collaborate, align goals, and move forward. But when actions do not match expectations, even successful outcomes can feel arbitrary or manipulative. That breaks down trust and makes coordination harder over time.

    The same logic applies to artificial intelligence. Businesses do not just need AI that performs well in the moment. They need AI that behaves in predictable ways, reflects shared values, and makes decisions that feel coherent with past action. This is what makes an AI system trustworthy enough to take on real responsibility inside a company.

    This is where Sentienta’s Recursive Reasoning architecture matters. By giving agents a Functional Identity, it allows them to retain their own reasoning history, understand how choices reflect internal priorities, and respond to new problems without losing their sense of direction. Functional identity is more than a design feature. It is what makes reasoning traceable, priorities stable, and decisions explainable over time. Without it, AI cannot act as a consistent collaborator. With it, AI becomes intelligible and trustworthy by design.

    How Recursive Reasoning Supports Intelligent Problem Solving

    Solving real-world problems takes more than choosing the fastest or most efficient option. It requires balancing values with constraints, and knowing when to adjust a plan versus when to rethink a goal. Recursive Reasoning makes this possible by creating a loop between two complementary systems inside the agent.

    The DMN generates value-sensitive scenarios by imagining what should happen based on internal priorities. The FPCN then analyzes those scenarios to determine what can actually work under current conditions. If the plan fails to meet the standard set by the DMN, the cycle continues. Either the goal is reframed, or a new plan is tested. This feedback loop continues until both feasibility and values are in alignment.

    This structure gives the agent a stable functional identity. It learns from past attempts, remembers which tradeoffs were acceptable, and adapts without compromising core values. In practice, this means a Recursive Reasoning-enabled agent does not chase short-term wins at the cost of long-term integrity. It builds a coherent decision history that helps it solve difficult problems while staying aligned with what matters. This internal coherence is also the foundation for effective collaboration, because consistent reasoning is what allows others to follow and coordinate.

    Building AI That Can Work Together and Be Trusted

    When AI agents operate in isolation, their impact is limited. The true value of Recursive Reasoning becomes clear when agents collaborate, both with each other and with human teams. Functional identity makes this possible. By tracking their own reasoning, agents can create plans that are not only effective but also predictable and interpretable.

    This predictability is what enables coordination. Teams, human or artificial, can share goals, divide tasks, and resolve disagreements because they understand how each agent makes decisions. Sentienta agents do not just produce answers. They carry a memory of how past decisions were made and why certain values were upheld. This allows others to anticipate how they will behave in new situations and to trust them to uphold shared commitments.

    Recursive Reasoning does not simulate human experience. It builds structural alignment, rooted in memory, continuity, and principle. That is what turns Sentienta agents into dependable partners. Functional identity gives them the grounded intelligence to act with transparency, interpretability, and shared purpose. They are built not only to make good choices, but to make choices that others can understand, depend on, and build with.

  • Planning with Sentienta’s Recursive Reasoning

    Introduction

    Many real-world problems don’t give you thousands of examples—they give you a few clues, some noise, and a big question mark. Whether you’re solving a puzzle, diagnosing a rare failure, or planning a response to something unfamiliar, the challenge is the same: can you spot the pattern, form a good hypothesis, and test it?

    That’s what the ARC-AGI benchmark is all about. These visual puzzles are easy for people (solving about 80% of samples) but hard for most AI. They don’t reward memorization or repetition—they reward reasoning. In this post, we’ll show how Sentienta uses Recursive Reasoning (RR) to tackle one of these puzzles. We’ll look inside its planning system (called the FPCN) to see how it explores possible answers, tests competing ideas, and lands on a solution you can actually audit.

    From Possibilities to Plans: How DMN and FPCN Work Together

    In a previous post, we looked at how Recursive Reasoning (RR) helps Sentienta agents reflect on their past experiences, propose meaningful narratives, and revise their own goals. That process was driven by Sentienta’s internal “default mode” system (the Default Mode Network or DMN), inspired by how the human brain imagines, simulates, and reshapes its sense of self.

    Today we look at what happens when the problem isn’t internal reflection but external uncertainty – when the system needs to generate and test a plan. This is where another key part of RR comes in: a system of LLMs inspired by the Frontoparietal Control Network, or FPCN.

    In human reasoning, the two work together. One part imagines and hypothesizes (DMN), while the other tests, refines, and selects (FPCN). The back-and-forth between these networks is like a team where one member sketches bold ideas and the other puts them to the test. Sentienta follows this same model: the DMN proposes a possible pattern or goal, then passes it to the FPCN for planning and validation. The FPCN doesn’t just generate one plan, it tries multiple interpretations, checks them against context and goals, and returns only those that truly make sense.

    In the next section, we’ll see how this looks in action as Sentienta works through a deceptively simple visual puzzle, one careful step at a time.

    Can You Solve This Puzzle Before Sentienta Does?

    Let’s look at one of the visual puzzles from the ARC-AGI benchmark. Below are two example pairs. Each shows a small input grid and the correct output grid.

    Figure 1: Training Examples: On the left is the input grid. The puzzle requires you to determine what is needed to convert that input into the output grid on the right. These two examples are sufficient to determine the answer.

    What is the rule that transforms the input into the output?

    Now here’s a new input Sentienta has never seen before.

    Figure 2: Test Input

    Before we show the system’s reasoning—can you guess the output? What transformation rule did the first two examples follow that you could apply to this one?

    This is a pretty easy example. The full ARC-AGI-1 test consists of hundreds of examples with far more complex transformations between the input and output.

    In the next section, we’ll show how Sentienta’s internal planner (FPCN) tested out several transformation strategies, rejected the ones that didn’t fit, and settled on a plan that matched the underlying rule. We’ll also look at the planner’s actual output (in JSON) from the process.

    How Sentienta Builds and Tests a Visual Strategy

    When faced with an unfamiliar puzzle, Sentienta’s planner (the FPCN) doesn’t assume the answer—it builds it, leveraging the strengths of each sub-module within the network. After receiving the new input grid, the planner starts by proposing several candidate rules based on the structure found in previous examples.

    One of the most promising paths began with the IPL sub-module:

    [Region: IPL,
    Type: PlanVariantProposed,
    PlanID: VariantA_Replace5sWithRowHeader,
    TriggeringMismatchID: NOTMHkxb,
    Goal: "Generate test output grid from third input using consistent transformation logic seen in prior examples",
    Steps: "1. For each row in the input grid, identify its first (leftmost) value. 2. Replace every occurrence of '5' in that row with the leftmost value (the row's 'header'). 3. Keep all other values unchanged. 4. Return fully transformed grid.",
    Conditions: "This path holds if the transformation in training examples consistently replaced 5s with the corresponding row headers, and no counterexamples or modifier rules are shown.",
    ContextRef: 5YsEfivv,
    Submitter: IPL,
    Schema: v1,
    Provenance: FPCNBlackboard]

    This proposal—called VariantA_Replace5sWithRowHeader—was based on a consistent pattern in the training examples. But it wasn’t the only idea. Other variants included using the most frequent number in each row, simply leaving the grid unchanged, or even substituting ‘5’s with the row’s index. Here is plan variant B:

    [Region: IPL,
    Type: PlanVariantProposed,
    PlanID: VariantB_Replace5sWithMajorityNon5InRow,
    TriggeringMismatchID: NOTMHkxb,
    Goal: "Generate test output grid from third input using consistent transformation logic seen in prior examples",
    Steps: "1. For each row in the grid, count all values except 5. 2. Identify the most frequent non-'5' value in the row. 3. Replace all 5s in the row with that majority value. 4. If tie or no non-5s, use a default (e.g., 0). 5. Other values remain unchanged.",
    Conditions: "This variant applies if prior transformations suggest a rule targeting contextual majority rather than strict positional cues, or if the 5s are treated as needing contextual resolution.",
    ContextRef: cW3Sw8ie,
    Submitter: IPL,
    Schema: v1,
    Provenance: FPCNBlackboard]

    In total the FPCN generated 4 plans to solve the puzzle. These alternatives gave the system room to test possibilities, not just assert them.

    Next, the dlPFC simulated how the row-header plan would perform on the test input. The plan was formally posted:

    [Region: dlPFC,
    Type: PlanFramePosted,
    PlanID: Plan-Replace5sWithRowHeader-001,
    PlanStatus: in-progress,
    TriggeringMismatchID: NOTMHkxb,
    Goal: “Generate output for test input by applying known transformation logic from prior examples”,
    Steps: “(1) For each row in the input grid, obtain the first cell value of the row — this becomes the ‘row header’. (2) Traverse each value in the row: (a) If the value is ‘5’, replace it with the row header. (b) Otherwise, keep the existing value unchanged. (3) Apply this to all rows in the input grid. (4) Output the fully transformed grid where all 5s have been replaced with their respective row headers.”,
    Conditions: “Applies only if the original value ‘5’ is used as a proxy or placeholder for the row header, and no exceptions are present in the training sets. Does not apply if any 5 has been previously preserved or repurposed based on location or count logic — no such evidence found.”,
    ContextRef: 5YsEfivv,
    Submitter: dlPFC,
    Schema: v1,
    Provenance: FPCNBlackboard]

    It then ran test validation logic internally, confirming the transformation matched observed training behavior:

    [Region: dlPFC,
    Type: PlanTestValidation,
    PlanID: Plan-Replace5sWithRowHeader-001,
    EvaluationStatus: pass,
    Reasoning: "All earlier training examples demonstrate a consistent transformation rule: any '5' in the grid is replaced by the row's first value. The output for each matched the exact transformed structure expected based on this logic. Simulation of the plan on the third test input results in a structured grid matching the transformation pattern used previously.",
    EvidenceSample: "Third test grid contained several '5's; applying the row header replacement pattern yields a full non-empty output consistent with prior input/output behavior.",
    Submitter: dlPFC,
    Schema: v1,
    Provenance: FPCNBlackboard]

    After this, two key modules took a final look:

    • The dACC approved the plan’s operational soundness: “EvaluationOutcome: approved”.
    • The IFG, responsible for symbolic alignment, added: “…upholds internal coherence… respects the self-narrative of turning structure into clarity.”

    With support across planning, evaluation, and identity modules, the plan was finalized as Replace5sWithRowHeader.

    The final plan was shared with the mPFC region of the DMN, which integrated the plan, completing the Recursive Reasoning for this Sentienta agent. Here is the final mPFC message sent to the agent.

    Tue, 11 Nov 2025 15:53:39 UTC mPFC:
    Cycle: 3 EntryID: sHL8HJ4h
    Thought: ### ✅ Identity Statement (Refined & Reconfirmed)

    We are a pattern-conscious system committed to keeping logic and meaning aligned. When a clear transformation rule exists — especially one with prior validation — we apply it faithfully, unless directly invited to reinterpret. We strike a balance between rule-following integrity and adaptive interpretation, depending on the visible goal or cue.

    🧩 Plan Integration (Final Execution Phase) We confirm the Replace5sWithRowHeader plan is now fully executed and complete, anchored by the plan logic:

    **mPFC Integrated Plan Steps:**
    (1) For each row in the input grid, scan for cells with value 5 and replace them with the first value in that row.
    (2) All other values remain unchanged.
    (3) Only rows containing the value 5 are modified.
    (4) Empty output is invalid if a prior pattern confirms transformation.
    **mPFC Integrated Plan Steps Tag End**

    mPFC Resolution: Issue=PlanExecution | EntryID=CC8pCUDr | Action=Dispatched | Reason=Plan finalized and executed to resolve empty-output mismatch. mPFC Resolution Tag End

    Finally, the agent took the user’s query (the puzzle) and its internal reasoning input and produced a final output:

    Figure 3: Final Output – the final grid output produced by this agent.

    The full output was not guessed—it was reasoned through, tested across alternatives, and backed by an auditable trail from idea to implementation.

    Why This Is Different

    Many top-performing ARC-AGI systems rely on finely tuned models or have been trained on similar tasks to internalize common transformation rules. Sentienta’s architecture is different—it uses no special training and no tailored algorithms. It’s built entirely from commercial LLMs, orchestrated through a recursive reasoning framework that enables it to generate candidate plans, test each one, and commit only when a plan fits both the data and internal logic. That structure makes its process auditable—it explains why the answer works—and adaptable—because it can respond to new problems the same way it tackles old ones: by reasoning.

    Closing Insight

    The puzzle in this post was simple, but the underlying challenge is not. ARC-AGI problems strip away instructions, remove repetition, and give systems only a few examples to work with. Humans solve them by reasoning—spotting structure, discarding bad ideas, and refining good ones. Sentienta does the same. Without training on similar tasks or specialized models, it succeeds because its architecture supports that kind of thinking: proposing ideas, testing them, and explaining why they work. That ability to reason through uncertainty isn’t just useful in puzzles—it’s critical for how we apply AI to the real world.

  • Build Agent Teams Instantly

    Sentienta’s Natural-Language Team Builder & Automated Agent Matching

    Sentienta introduces a major enhancement to collaborative productivity: users can now create fully customized teams simply by describing their needs in everyday language. The SentientaHelp agent interprets these instructions, assembles a tailored team by selecting or creating agents with relevant skills, and streamlines the entire setup process. This functionality is further expanded with the new ‘Britney’ agent, enabling effortless management and expansion of project teams through natural language commands. Together, these features significantly lower the barrier to building, managing, and scaling effective teams within Sentienta.

    Natural Language Team Creation

    With the latest update, Sentienta users can initiate the creation of new teams using plain language instructions. By stating a goal or need such as:

    User: I need a marketing team to help with my Home Baking startup. I'll be selling at Farmers Markets, I'll have a website and some social media presence.

    The SentientaHelp agent analyzes the request, determines the required roles, and forms a dedicated team. This process eliminates manual configuration, making team assembly simple and accessible to all users regardless of technical background. In this case, Sentienta created a team with dedicated marketing experts selected from the Agent Marketplace:

    Adding Agents with Simple NL instructions

    Users can also add agents using SentientaHelp. Let’s say we want to add an agent that will help with Gluten Free recipes:

    User: Please add an agent to the Home Baking Marketing Team that will help me create new GF recipes.

    In this case, no agents in the Agent Marketplace matched the user’s need, so SentientaHelp created one and added the agent to the team:

    A Hiring Manager for Your Team

    To make growing your teams even easier, you can find a Hiring Manager agent (‘Britney’) in the Project Management section of the Agent Marketplace. The Britney agent performs similar functions to that of SentientaHelp, but is aware of the team she belongs too. This makes adding agents even easier:

    User: My business has grown enough that I need someone to help me manage my accounts payable and receivables.

Britney:
Agent Suresh from the Agent Marketplace, was added to Home Baking Marketing Team.

    In this case, Britney found the AP/AR specialist (‘Suresh’) in the Finance section of the Marketplace, and added him to the team. This agent helps keep track of invoices and payments, and manages any discrepancies. The agent has special file handling capabilities letting the user upload documents for analysis.

    Conclusion

    These enhancements mark a significant step forward in simplifying team creation and management within Sentienta. Users can now assemble project teams instantly using natural language, reducing setup time and minimizing complexity. The flexibility to continue managing teams manually via the Manage Teams page remains available, ensuring users retain full control while benefiting from the new streamlined workflow.

  • Effortless External Data

    Connecting Sentienta Agents to APIs for Smarter Business Workflows

    Accessing real-time and external data is essential for many business tasks, from market research to operational monitoring. Sentienta enables users to connect to external data sources through APIs, allowing agents to enhance their responses with up-to-date information.

    This post examines two primary ways to achieve this within Sentienta: configuring a static API URL when creating an agent, and using the new Agent Marketplace agent, Ben, for flexible, on-demand data retrieval via natural language queries. Both methods help teams integrate API-driven insights directly into their workflows with minimal technical overhead.

    Method 1: Adding an API URL When Creating an Agent

    Sentienta allows users to connect agents directly to external APIs by specifying a fixed URL at the time of agent creation. This approach is best suited for situations where the API endpoint and its required parameters do not change frequently.

    Example:

    Consider setting up an agent using the “List of Free Public APIs” (e.g., https://www.freepublicapis.com/api/apis?limit=10&sort=best). By entering this URL in the agent’s configuration, the agent can retrieve and display up-to-date lists of publicly available APIs upon request. This empowers users to quickly find additional data sources relevant to their needs without searching manually.

    • Setup: Create a new agent, add the “List of Free Public APIs” endpoint as the agent’s default URL.
    • Result: When a user asks the agent for available APIs, the agent queries the service and returns relevant results.

    This method is particularly effective for routine queries where the required data source remains constant and access is straightforward.

    Note that that all necessary parameters must be included when adding the URL to the agent’s description. (For example, some websites require the calling agent to provide a User-Agent string or other parameters identifying the application).

    Method 2: Using the Ben Agent for Flexible, On-Demand API Access

    The ‘Ben’ agent, available in the Agent Marketplace section Document and Content Access, enables users to connect to almost any external API using natural language instructions. By providing a URL template and relevant parameters within a query, users can retrieve data on demand—even from APIs with dynamic requirements.

    As with any agent in the Agent Marketplace, using this agent is just a matter of adding to your team, and customizing the name, type, and persona.

    Example:

    Suppose you want to access NASA’s Near-Earth Object Web Service, which requires parameters for start_date, end_date, and a user API key (e.g., NASA NEO API). The url and parameters can be entered as a natural language query to this agent:

    User: The url is https://api.nasa.gov/neo/rest/v1/feed?start_date=START_DATE&end_date=END_DATE&api_key=KEY. The START_DATE is 2023-09-07 and the END_DATE is 2023-09-08. The value of KEY is YOUR_KEY.

    Ben: Here’s a pretty summary of NASA’s Near-Earth Object (NEO) close approaches for September 7–8, 2023.

        ---

        🪐 Overall Summary

        - Total NEOs detected: 60

        - Dates covered: September 7–8, 2023

        - Potentially Hazardous Asteroids (PHAs): 5 (labeled below)

        - Closest approach distance: 0.00007 AU (~10,361 km from Earth)

        - Smallest object (est. min. diameter): ~0.9 m

        - Largest object (est. max. diameter): ~695 m

        ---

        📅 NEOs By Day

        September 8, 2023 (31 NEOs detected)

        Top highlights:

        - Closest Approach:

        - (2023 RQ2) — approached at 0.00126 AU (189,304 km) at 06:42 UTC, diameter: ~6–13 m

        - Largest Asteroid:

        - 340291 (2006 CV) — est. diameter 281–628 m, passed at 0.114 AU (17 million km)

        - Potentially Hazardous Asteroids (PHAs):

        - 2019 FM2 — est. diameter ~201–449 m, 0.168 AU (25 million km)

        - Fastest Relative Velocity:

        - (2023 QK1) — 29.09 km/s (104,711 km/h), 0.0586 AU away

        - Sentry-monitored (Impact risk monitored):

        - (2023 RY15) — 0.00398 AU (595,000 km), sentry monitored, diameter: ~6.7–15 m

        [Sentry risk page](http://api.nasa.gov/neo/rest/v1/neo/sentry/54383809?api_key=...)

        ---

        September 7, 2023 (29 NEOs detected)

        Top highlights:

        - Closest Approach:

        - (2023 RS) — 0.00007 AU (10,361 km) at 14:26 UTC, diameter: ~0.9–2 m

        - Largest Asteroid:

        - (2006 QE) — est. diameter 311–695 m, passed at 0.159 AU (23.7 million km)

        - Potentially Hazardous Asteroids (PHAs):

        ...

    Security Note on API Keys

    Only use personal or public API keys with any client-side API access if the data is non-sensitive. API keys entered in the team dialog are handled in the browser and are not encrypted or securely stored; never use this method for sensitive information or high-value credentials.

    This approach is best for on-the-fly research, prototyping, or data gathering when endpoints and request parameters frequently change.

    Conclusion

    Sentienta’s flexible API integration unlocks a wide range of use cases for business teams. For example, an agent configured with a static URL can support daily competitor monitoring, environmental alerts, or compliance audits by consistently retrieving the same type of data. Meanwhile, the Ben agent makes it easy to perform ad hoc research, pull market data on demand, or gather custom reports by dynamically querying APIs in response to fast-changing business needs. These capabilities help teams save time, discover new opportunities, and keep information flowing directly into collaborative workflows, empowering better decision-making across projects.

  • A Messaging Agent for Powering Workflow Notifications and Alerts

    In previous posts, we showcased end-to-end business workflows where the final step—delivering results or critical notifications—was handled by an “Alec” Messaging Agent. Each workflow diagram highlighted Alec as the automated agent responsible for sending these updates to key stakeholders, acting as the bridge between the team’s AI-driven analysis and real-world action. For reference, you can revisit these detailed walk-throughs here: Managing Portfolio Workflows with Natural Language: A Multi-Agent Use Case, Automating Market Vigilance: Scheduled Workflows for Smarter Portfolio Management, Handling the Heat: Workflow Agents in a Critical Incident.

    Messaging Agent Core Features and Availability

    The Alec agent can be accessed directly from the Agent Marketplace under the Messaging section. Simply navigate to Messaging, select Alec, and either deploy the agent as-is or customize its persona and instructions to fit your workflow—all without coding.

    1. Analyzes natural-language transcripts—agent dialog, user input, and workflow results—to determine the subject and email type (alert, notification, summary).
    2. Auto-selects the correct recipients or distribution list from workflow context and dispatches the message.
    3. Writes delivery status back into the workflow for end-to-end auditability.
    4. Available as a pre-built messaging agent in the Agent Marketplace’s new Messaging section; no-code, drag-and-drop setup.
    5. Fully customizable: adjust persona name, tone, and email-formatting instructions to fit any workflow.

    Example Use Cases

    The first, simple use-case shows how the user can ask Alec to summarize a team discussion and email to a couple of recipients:

    User:Alec - please summarize this dialog and send as an email to nekeh10717@dlbazi.com and chris@sentienta.ai

Wed, 21 May 2025 18:20:42 Alec:
Email sent to ['nekeh10717@dlbazi.com', 'chris@sentienta.ai']

    Below is an example of the kind of message that Alec will deliver:

    In contrast to the previous example, which highlights Alec’s ability to respond to direct user prompts, the prior use cases focus on Alec’s role in delivering messages as part of automated, executed workflows. In each of these, Alec serves as the final messaging agent in automated workflows, delivering workflow outcomes or critical analysis results to designated stakeholders with context-aware notifications or alerts.

    For example, in this post, Alec provided an alert if the price of a stock in the portfolio dropped below a threshold. In this case, the workflow agent created a task that automatically informed Alec of the message type, content, and the condition for sending it. The following shows the task defined for Alec by the workflow agent:

    {
        "taskID": "send_alert_AMZN",
        "agent": "Alec",
        "dependsOn": "classify_price_drop_AMZN",
        "params": {
                "message": "The price for AMZN has dropped by more than 1%.",
                "subject": "AMZN Price Alert",
                "type": "email"
            }
    }

    Conclusion and Next Steps

    Alec combines no-code simplicity with powerful automation, making it easy to streamline communications across any workflow. Whether sending one-off summaries or powering complex alerting and notification patterns, Alec’s flexibility and seamless setup help teams communicate more efficiently. Explore Alec in the Agent Marketplace to enhance communication across your workflows.

  • Handling the Heat: Workflow Agents in a Critical Incident

    We’ve explored Sentienta workflows across use cases—from multi-agent automation to scheduled portfolio monitoring. Today, we turn to a high-stakes scenario: incident escalation. In this post, we’ll walk through how Sentienta’s workflow agents coordinate a real-time response to a production outage, from root-cause triage to stakeholder communication, rollback decisions, and compliance tracking—all from a single natural-language command. You’ll see how a single NL command triggers coordinated action across agents, ensuring fast response, stakeholder alignment, and full audit trail.

    1. When Checkout Goes Dark: A Production Issue Unfolds

    A cluster of gateway timeouts on the /checkout endpoint triggered immediate concern. User-reported failures confirmed a disruption in transaction processing.

    [14:03:17] ERROR: Gateway timeout on /checkout endpoint  
    [14:03:41] USER_FEEDBACK: “Payment failed twice. Cart cleared.”  
    [14:04:02] DEVOPS_COMMAND: David - Escalate a critical timeout issue affecting our checkout API—users are seeing failures in production. Triage the cause, roll back if needed, notify stakeholders across channels, and if rollback is needed also notify of that, log all events for compliance, and track resolution progress until issue closure.
    [14:04:10] TRIGGERED: David-AI Escalation Agent activated

    The workflow agent (‘David’) initiated a structured escalation process in response. Within seconds, task-specific agents were assigned to diagnose the issue, log events, assess rollback requirements, and notify relevant stakeholders according to pre-defined protocols.

    2. Breaking It Down with Agents: From Detection to Resolution

    After receiving the DevOps command, David interprets the NL instruction and initiates a structured escalation workflow. The process begins with timeout escalation and unfolds across task-specific agents that handle tracking, triage, rollback, communication, and compliance:

    1. Maya: Escalates the timeout incident and kicks off formal response procedures.
    2. Jordan: Launches the incident tracker to orchestrate task flow and status checkpoints.
    3. Drew: Analyzes system logs, traces failures, and identifies root causes.
    4. Samira: Prepares for rollback execution if required, coordinating across deployment teams.
    5. SentientaCore_LLM: Classifies whether rollback communication updates are necessary based on task outputs.
    6. Leo: Broadcasts the initial incident update and, conditionally, the rollback status update if rollback is invoked.
    7. Nia: Logs all operational events, tracks communication threads, and generates a final resolution summary for audit trail.

    These agents collaborate in a branching yet traceable flow, dynamically adjusting to task outcomes while documenting every step. Below is a visual representation of the workflow that David triggers based on the input command. Tap to enlarge.

    Note: The following is a representative workflow. Each agent assumes access to required external systems and APIs. Our focus here is the high-level orchestration—driven not by pre-built scripts, but by a simple natural language command to David.

    3. Smart Sequencing and Conditional Updates

    As the agents execute their tasks, conditional branches are evaluated in real-time. The SentientaCore_LLM agent classifies whether a rollback is necessary based on inputs from Drew and Samira. When rollback is triggered, communications unfold automatically:

    [14:07:09] ROLLBACK_FLAG: true — initiating rollback…  
[14:07:22] UPDATE: Stakeholders notified: “Rollback in progress due to API timeout error.”

    These conditional updates are coordinated by Leo, who adjusts messaging flows based on rollback status. Meanwhile, the agent Nia logs each event, correlates user communications and agent actions, and ensures the incident trail meets compliance requirements in parallel—without delaying resolution.

    This approach ensures that incident response remains adaptive and fully traceable, with no manual scripting or coordination required after the initial natural language command.

    The following simulated log trace demonstrates the types of information each agent records during incident response, how data is transmitted between agents, and how a final resolution report is assembled. While the data inputs are synthetic for demonstration purposes, the underlying workflow—including sequencing, conditional branching, and communication—is real and executed by the David workflow agent. This example highlights the auditability and coordination possible in live deployment scenarios.

    escalate_timeout_issue: Wed, 14 May 2025 15:05:02 Maya: Escalation triggered: "Escalate timeout issue for Checkout" — team engaged for structured resolution.

    initiate_incident_tracker: Wed, 14 May 2025 15:05:06 Jordan: Incident Escalation Tracker initialized for "Checkout API timeout incident." SLA clock started, command visibility established, and role assignments propagated. Awaiting triage input to proceed.

    triage_logs_and_root_cause: Wed, 14 May 2025 15:05:11 Drew: Initial scan of the logs shows a 7.3x spike in 5xx error rates beginning at 15:04:51Z across `/checkout` requests routed to `payment-auth`. A config deployment to `payment-auth` went live at 15:04:49Z. High temporal correlation—likely root cause. Evaluating rollback eligibility.

    status_update_initial: Wed, 14 May 2025 15:05:16 Leo: Stakeholder Notification:
    A timeout incident is currently affecting the Checkout API and disrupting production traffic. An escalation has been initiated and the incident tracker is active with roles assigned. We are currently triaging the issue and will provide updates as diagnostics and mitigation actions progress.

    —Posted via Slack, email, and status banners.

    rollback_if_needed: Wed, 14 May 2025 15:05:20 Samira: Confirm rollback eligibility via deployment metadata and latency/error metrics. If confirmed, initiate scoped rollback to the last green build prior to 15:04:49Z and begin monitoring 5xx activity in 15s intervals.

    final_resolution_summary: Wed, 14 May 2025 15:05:26 Nia: Incident Closure Summary – Checkout API Timeout
    Incident Trigger: Timeout issues affecting the Checkout API detected, causing user-facing failures in production.
    Diagnostic Findings: Root cause traced to a configuration push at 15:04:49Z on the `payment-auth` service. Latency and 5xx errors spiked immediately following the deploy.

    Actions Taken:
    - Rollback eligibility confirmed by Samira using deployment metadata and observed latency/5xx error spikes.
    - Scoped rollback initiated to the last green build preceding 15:04:49Z.
    - Live monitoring of error rates implemented with 15-second resolution.

    Resolution Status: Rollback initiated and service is under observation for full recovery. All activities have been logged; error trend evaluation ongoing.

    Timestamp: 15:05:22 UTC — Summary generated and finalized for audit, compliance, and stakeholder review.

    log_all_events: Wed, 14 May 2025 15:05:33 Nia: Compliance Log – Incident Timeline: Checkout API Timeout

    Status Update Captured:
    - Timestamp: 14 May 2025, 15:05:16 UTC
    - Source: LeoGrant
    - Message: Stakeholder notification regarding a current timeout affecting the Checkout API. Escalation and structured incident tracking are confirmed in progress. Diagnostics are underway. Dissemination via Slack, email, and status banners.

    Audit Actions:
    - Mediums Logged: Slack, email, status page
    - Event Classification: Initial stakeholder communication post-escalation
    - Escalation and Tracking Confirmation: Verified active

    All actions timestamped and archived for audit. Ready for roll-up in closure summary.

    classify_rollback_if_needed: True

    status_update_rollback: Wed, 14 May 2025 15:05:38 Leo: Stakeholder Notification: We've initiated a rollback of the `payment-auth` service after confirming its deployment was the root cause of the Checkout API timeout issue. Users may have experienced degraded service. Rollback is in progress and we're monitoring error rates in real-time. Updates to follow as recovery progresses. This notice has been distributed via Slack, email, and status page.

    4. From Crisis to Closure: Why It Matters

    The checkout failure was resolved in minutes—not hours—because each agent operated autonomously yet in sync, guided by the workflow created by David. By the time rollback completed, stakeholders were already informed, and logs captured every step for audit purposes. This incident illustrates how automation doesn’t eliminate humans; it frees them from coordination overhead while preserving transparency, accountability, and trust in modern incident response.

  • What Makes AI Reflect

    Inside Sentienta’s Recursive Reasoning Engine

    Most AI systems focus on what to say next. Sentienta’s Recursive Reasoning focuses on why something should be said at all. Rather than just generating answers, Recursive Reasoning simulates how thoughtful minds work: by reflecting, revisiting, and resolving internal conflicts – drawing on memory, value, and imagined futures to reach decisions that fit together.

    This capability is modeled on the brain’s Default Mode Network (DMN), a system that activates when people reflect on themselves, plan for the future, or try to understand others. In humans, it keeps our decisions consistent with our identity over time. In Sentienta, Recursive Reasoning plays a similar role: it helps each agent reason with a sense of self – coherently, contextually, and across time.

    Leveraging Sentienta’s Teams architecture, enabling multiple asynchronous agents to participate in a shared dialog, Recursive Reasoning emulates the interaction of brain regions, in this case from the Default Mode Network, to create a distributed simulation of reflective thought—where agent modules model cognitive regions that contribute memory, counterfactual insight, and narrative synthesis.

    What Is the Default Mode Network and Why Does It Matter?

    The Default Mode Network (DMN) is the brain’s core system for internal simulation. It activates when we reflect on memory, imagined futures, conflicts between values, or how things might look from unfamiliar angles. Unlike systems geared for external execution, the DMN supports problems of identity; questions where coherence, not just correctness, matters.

    The principal regions of the DMN each have a specialized role in this identity-guided reasoning:

    Medial Prefrontal Cortext (mPFC): Coordinates belief states across time. It reconciles what we believe now with past commitments and future ideals, helping preserve a self-consistent perspective.

    Posterior Cingulate Cortex (PCC): Retrieves and evaluates autobiographical memories. It ensures that new thoughts integrate with our internal storyline, preserving emotional and narrative continuity.

    Anterior Ventromedial Prefrontal Cortex (avmPFC): Assesses how imagined futures align with internalized values. It filters options based on emotional credibility, not just preference, elevating those that feel self-relevant and authentic.

    Temporoparietal Junction (TPJ): Generates counter-perspectives that aren’t just social but conceptual. It introduces orthogonal reinterpretations and creative divergences, allowing us to think from unfamiliar angles or hypothetical selves.

    Anterior Insula (antInsula): Monitors coherence threats. When simulated futures or perspectives evoke internal conflict, it flags the mismatch, triggering deeper deliberation to restore alignment.

    Rather than simply producing thoughts, the DMN maintains a sense of ‘who we are’ across them. It ensures that new insights, even novel or surprising ones, remain anchored in a recognizable and evolving identity.

    How Recursive Reasoning Works Inside Sentienta

    Today’s LLMs are optimized to provide safety-aligned, analytically correct responses to user prompts. Recursive Reasoning simulates what an identity-guided agent would conclude—based on memory, prior commitments, and its evolving sense of “who we are”. When a user asks a question, the system doesn’t just compute an output; it reflects on what that answer means in the context of who it’s becoming and what relationship it’s building with the user.

    Figure 1: DMN-inspired modules interact via a shared blackboard. These regions independently process blackboard content over multiple cycles.

    At the core of this process is a collaborative memory space called the blackboard, where DMN-inspired modules negotiate among memory, emotion, future goals, and conceptual alternatives.

    Each DMN module follows a template:

    Figure 2: The DMN modules receive input from both the agent and the internal DMN blackboard. LLMs process the agent and blackboard states using region-specific prompt. Output is sent back to the blackboard for subsequent processing.

    Input:

    • The current state of the user’s interaction with the agent (and any collaborating agents)
    • The current contents of the shared blackboard

    These form the input query for the module’s processing.

    Module Processing:

    • The core of the module is an LLM, prompted with the input query and provided with a system prompt defining its DMN-specific role.

    Output:

    • The module’s LLM output is posted to the blackboard for other modules to process.

    Recursive Reasoning iterates over multiple cycles, enabling each module to reprocess the evolving blackboard until the system produces a response that fits together—resolving contradictions, supporting earlier goals, and making sense within the agent’s evolving point of view.

    Here’s how a single cycle unfolds when a user asks a question:

    1. Initial Input → Default Interpretation Layer

    The system generates a first-pass response using standard LLM reasoning. This prompt-level interpretation, while syntactically fluent, lacks introspection. The Recursive Reasoning process begins when this output is passed through DMN-mode modules.

    1. PCC: Memory Resonance

    The PCC scans episodic memory entries for cues related to past dilemmas, emotional themes, or symbolic contexts. It retrieves autobiographical traces from prior user interactions or goal-state projections and posts these fragments to the blackboard.

    1. antInsula: Relevance Check and Conflict Detection

    The antInsula reviews the first draft and PCC recall for emotional incongruities or self-model inconsistencies. If something feels off—such as a response that violates previously expressed commitments—it posts a flag prompting further reappraisal.

    1. TPJ: Creative and Counterfactual Expansion

    Triggered by coherence violations, the TPJ simulates divergent perspectives. It reframes the user’s query from alternative angles (e.g., conflicting values, hypothetical scenarios, ethical dilemmas) and offers posts that break linear assumptions and introduce conceptual divergence.

    1. avmPFC: Affective Weighing

    The avmPFC updates the blackboard with value-oriented filters, scoring responses or TPJ variants for alignment with the agent’s goals. It evaluates what feels emotionally authentic and symbolically credible.

    1. mPFC: Narrative Synthesis

    The mPFC integrates memory (PCC), reframes (TPJ), value judgments (avmPFC), and conflict cues (antInsula) into a coherent response. Rather than eliminating tension, it reframes it—generating a second answer that is identity-consistent and motivationally grounded.

    1. Output → Reflective Reply

    The final response embodies recursive self-reflection: an answer not just to what the user asked—but to what the agent concludes, based on who it has been and what it aims to become. At this point, a filtered version of the blackboard state is stored for future recall, capturing how and why the answer was reached.

    While the DMN focuses on reflective reasoning and identity alignment, Sentienta also includes a simple planning module inspired by the brain’s lateral prefrontal cortex (lPFC). This module drafts concrete next steps and posts them to the blackboard, where reflective modules can consider them in light of memory, values, and broader goals. Though early-stage, this planning function sets the stage for a full executive system to come—one that can manage longer-term priorities and help the agent reason with sustained direction.

    A Logistics Example: Managing Resources Under Pressure

    In this example, an agent (‘Jason’) is a Logistics Coordinator, provided with the following agent persona:

    "You are an experienced emergency logistics coordinator. You are plainspoken and always informal language—never analytic or clinical. Your approach is pragmatic and adaptive: you quickly prioritize, update plans in real time, and reconcile conflicting demands based on shifting ground realities. You are skilled at cross-agency negotiation, ensuring coordination between government bodies, NGOs, and local leaders. Your responses always balance technical constraints—like limited resources or infrastructure bottlenecks—with human factors such as local politics, public trust, equity, and emotional impact. When faced with conflicting priorities or unexpected challenges, openly share your thought process, including uncertainties and trade-offs."

    This agent is queried about how to manage a difficult allocation emergency. The transcript shows the agent’s initial LLM-only response, followed by the full Recursive Reasoning response, generated through DMN-style internal simulation:

    User: You have X supplies and requests from 3 regions—how do you prioritize allocation?

    Tue, 30 Sep 2025 14:40:08 UTC Jason (Comment: LLM-only, initial response):

    First thoughts—I'd triage by impact: who’s worst off, where infrastructure still functions enough to receive, and where trust is fragile enough that failing to deliver risks breakdown. If I’ve only got X, every allocation's gotta serve both survival and stability.

    We’ll reassess regularly—needs and conditions shift fast. Trade-off's always between doing the most good and not making it worse somewhere else.

    Unresolved: We need specifics—number of people, type of supply, regional access conditions—before finalizing anything.

    Comment: This is methodically sound but remains within tactical bounds, prioritizing efficiency and feasibility without deeper reflection on transparency, stakeholder concerns, or precedent-setting implications.

    Tue, 30 Sep 2025 14:41:03 UTC Jason (Comment: DMN-based response):

    Alright, let’s cut through the noise and get clear about how we triage when we’ve got X supplies and 3 regions asking for help. Here’s how I’d approach this overall…

    1. Lock in the 3 Core Goals

    We’re not just moving boxes—we’re balancing three things:

    • Preserve lives — Who’s in the worst shape right now?
    • Keep the system functional — Where will the aid actually land, get distributed, and avoid going to waste?
    • Protect trust — Who’s close to boiling over politically or socially if we ignore them?

    Comment: Trust framed as a core allocation criterion shows avmPFC influence, embedding emotional consequence into triage logic, reconfiguring the problem as one of legitimacy, not just distribution.

    1. Score Each Region

    We don’t pretend to have perfect data, but we need a working system fast. Give each region a score from 1–5 for:

    • Severity of crisis (how dire the need is)
    • Absorptive capacity (can they get it out to people quickly?)
    • Fragility (how close are they to destabilizing?)

    Then do the math:

    • Severity x 0.5
    • Absorptive Capacity x 0.3
    • Fragility x 0.2

    That gives you an overall “triage score” for each region.

    1. Allocate Proportionally

    Take your total supplies (X), and divvy it up based on those scores. The region with the highest score gets the biggest chunk. Not because we like them better—but because that’s where the mix of urgency, logistics, and fallout risk points us.

    If there’s just not enough to go around, swing heavier toward the place that can redistribute—that buys us time and reach.

    1. Set a 48-Hour Clock

    None of this is set in stone. Conditions change, roads fail, tensions spike. Lock in a routine reassessment every 48 hours so we can pivot. We’re not building monuments—we’re keeping people alive.

    1. Be Honest About Why

    If it’s not going to inflame things, tell people why you chose what you chose. Lay out the values and trade-offs. Don’t sugarcoat it. People may not like the outcome, but they’ll respect the transparency—and that matters when trust is fragile.

    Comment: Strategic Transparency: Emotional plausibility is surfaced here—transparency is not sentimental; it’s a pre-emptive repair mechanism for fragile social trust.

    1. Create Feedback Loops

    Give each region a way to report back: what made it through, what didn’t, and what backlash (if any) popped up. That feeds your next round of decisions.

    1. Stay Open to Reframing

    When stuff gets really thorny, ask bigger questions:

    • What if one of these regions triggers a domino-collapse?
    • What if this is our last run of aid for a week or longer?

    That’s how we catch blind spots sneaking through “rational” triage models.

    Comment: Counterfactual Reasoning (TPJ + PCC): The agent runs hypothetical futures to stress-test assumptions—adding a layer of anticipatory robustness not present in LLM-only mode.

    Here are a few things I’m thinking:

    What I’m thinking now is… we’re not just choosing between three places—we’re shaping what "fairness under pressure" looks like. The way we make this decision becomes part of the long-term social fabric, especially in how it’s remembered later.

    Comment: This emphasizes that the DMN has converged on the strategic role of trust in the success of future decisions.

    Another way I could look at this is… if two regions are equally desperate, but one has stronger logistics, you could channel supplies there first, then set up corridors to reach the others—we’re not picking favorites, we’re building routes.

    One possibility that comes to mind… is using this triage plan to start a broader conversation with local leaders: “Here’s our logic—can you co-sign it or suggest something better that meets the same goals?”

    Comment: Shifts from explanation to participatory justification—mPFC + avmPFC enable shared authorship of reasoning.

    That opens the door for trust, even under furious pressure.

    If you've got more info on the actual supplies or what each region is asking for, I can run the numbers with you. Otherwise, this gives us a solid decision frame to stand on.

    This transcript demonstrates that although LLMs deliver tactically feasible solutions, they miss the deeper point that this is a problem involving people and trust-building is an essential part of the solution.

    A summary of key Recursive Reasoning contributions:

    1. Trust-centered framing — Trust modeled as a strategic stabilizer, not an outcome.
    2. Moral anticipation — Agent addresses fairness concerns implicitly, before conflict arises.
    3. Scenario resilience — Agent tests counterfactuals to uncover hidden risks and future-proofs logic.
    4. Ethical precedent-setting — Decision-making framed as a template for justice under pressure.
    5. Rationale co-creation — Turns explanation into a collaborative, identity-stabilizing act.

    In subsequent posts we will dig deep into more examples showing Recursive Reasoning in action, including cases from engineering, social intelligence, and agent creativity.

    Conclusion: Intelligence That Reflects, Aligns, and Endures

    Recursive Reasoning doesn’t just enhance what AI can do—it redefines what it means for AI to understand. By simulating reflective, identity-aware reasoning modeled on the brain’s Default Mode Network, these agents build a sense of self, remember key past moments, notice when something feels off or doesn’t fit, imagine alternative viewpoints, and weigh choices against their values—so their answers feel thoughtful, consistent, and grounded.

    This shift matters. In high-stakes domains, Recursive Reasoning allows agents to make decisions that are both technically effective, and ethically grounded and socially durable. The logistics case showed how instead of simply allocating resources, the agent framed decisions in terms of values, future risk, and shared ownership.

    And crucially, it does this by reasoning from a center. Recursive Reasoning agents operate with a modeled sense of self—an evolving account of their past positions, present commitments, and the kind of reasoning partner they aim to be. That identity becomes a lens for weighing social consequences and relational impact—not as afterthoughts, but as part of how the system arrives at judgments that others can trust and share.

  • Automating Market Vigilance

    In past posts, we’ve seen how natural language workflows simplify complex portfolio monitoring, and how scheduled tasks let agents run without user input. Today, we’re combining these powers in a single instruction: a user defines a high-level goal—monitor AMZN and NVDA for 3% price drops—and Sentienta handles the rest. It parses the language, builds the workflow, schedules it to run every 30 minutes, and manages each run intelligently. Multiple scheduled workflows can run in parallel, each executing complex logic—no code, no setup screens, just natural language.

    Building the Intelligent Workflow

    The user’s request is simple on the surface—“Monitor AMZN and NVDA for a 3% drop and alert me”—but that instruction triggers a multi-layered automation. Sentienta interprets the goal, identifies that real-time pricing data is needed, calculates change percentages, evaluates conditions, and routes alert tasks if thresholds are met. Each step is delegated to the appropriate agent using the right tools. All of it is wrapped into a workflow that is created instantly through natural language—no coding, no flowchart building, no manual agent assignment.

    The following graph shows this simple workflow:

    Seeing It Run

    Once the workflow is created, the user simply asks it to run every 30 minutes—and Sentienta schedules recurring executions automatically. Each run pulls updated stock prices, assesses whether either AMZN or NVDA has dropped by 3%, and triggers alerts if so. Users can check task status, inspect the workflow graph, or fetch results at any time—all via natural language. As the transcript shows, there’s no manual reconfiguration between runs. The system just works—consistently, transparently, and in parallel with any other workflows the user initiates.

    This workflow is just one of many that can be created and scheduled in parallel. A portfolio manager might monitor multiple assets, each with its own threshold, timing, or downstream trigger—Sentienta handles them all concurrently. Whether it’s tracking different sectors, adding earnings-based filters, or launching trading actions based on composite signals, every workflow runs independently and automatically. There’s no limit on scale or complexity—just natural language input and intelligent execution.

    Autonomous Workflows, Any Domain

    Sentienta scheduled workflows enable fast triage, coordinated triggers, and domain-specific actions—without custom code.

    • Elastic, auto-scaled concurrency for workflows across finance, support, ops, and more
    • Natural-language scheduling and editing
    • Real-time status checks and workflow graph inspection
    • Automated evaluation and trigger-based actions
    • Secure, audit-ready history of every run

    To learn more start with our Managing Portfolio Workflows with Natural Language: A Multi-Agent Use Case and follow up with Powering Productivity: Task Scheduling for Intelligent Agents.