Sentienta Blog

Tag: AI collaboration

  • Beyond Chat: How OpenClaw and Sentienta Operationalize Multi‑Agent Work

    OpenClaw is having a moment—and it’s easy to see why. In the developer community, “desktop agents” have become the newest proving ground for what AI can do when it’s allowed to take real actions: browsing, editing, running commands, coordinating tasks, and chaining workflows together. OpenClaw taps directly into that excitement: it’s open, fast-moving, and built for people who want to experiment, extend, and orchestrate agents with minimal constraints.

    At the same time, a different kind of question is showing up from business teams and Sentienta users: How does this compare to what we’re already doing in Sentienta? Not as a “which is better” culture-war, but as a practical evaluation: what’s the right platform for the kind of work we need to ship reliably?

    The most interesting part is that both worlds are converging on the same core insight: a single, standalone LLM is rarely the best operating model for real work. The trend is clearly moving toward teams of interacting agents, specialists that can collaborate, review each other’s work, and stay aligned in a shared context. In other words, the wider market is starting to validate a pattern Sentienta has been demonstrating for business outcomes for over a year: multi-agent dialog as the unit of work.

    In this post we’ll look at what OpenClaw is (and who it’s best for), then quickly re-ground what Sentienta is designed to do for business users. Finally, we’ll cover the operational tradeoffs, especially the security and governance realities that come with high-permission desktop agents and open extension ecosystems, so you can pick the approach that matches your needs for simplicity, security, and power.

    What OpenClaw is (and who it’s for)

    OpenClaw is best understood as an open ecosystem for building and running desktop agents – agents that live close to where work actually happens: your browser, your files, your terminal, and the everyday apps people use to get things done. Instead of being a single “one size fits all” assistant, OpenClaw is designed to be extended. Much of its momentum comes from a growing universe of third‑party skills/plugins that let agents take on new capabilities quickly, plus an emerging set of orchestration tools that make it easier to run multiple agents, track tasks, and coordinate workflows.

    That design naturally attracts a specific audience. Today, the strongest pull is among developers and tinkerers who want full control over behavior, tooling, and integrations, and who are comfortable treating agent operations as an engineering surface area. It also resonates with security-savvy teams who want to experiment with high-powered agent workflows, but are willing to own the operational requirements that come with it: environment isolation, permission discipline, plugin vetting, and ongoing maintenance as the ecosystem evolves.

    And that’s also why it’s exciting. OpenClaw is moving fast, and open ecosystems tend to compound: new skills appear, patterns get shared, and capabilities jump forward in days instead of quarters. Combine that pace with local-machine reach (the ability to work directly with desktop context) and you get a platform that feels unusually powerful for prototyping—especially for people who care more about flexibility and speed than a fully managed, “default-safe” operating model.

    It is worth noting the recent coverage that suggests OpenClaw’s rapid rise is being matched by very real security scrutiny: Bloomberg notes its security is work in progress, Business Insider has described hackers accessing private data in under 3 minutes, and noted researcher Gary Marcus has called it a “disaster waiting to happen“.

    A big part of the risk profile is architectural: desktop agents can be granted broad access to a user’s environment, so when something goes wrong (a vulnerable component, a malicious plugin/skill, or a successful hijack), the potential blast radius can be much larger than a typical “chat-only” assistant. Not all implementations have this risk, but misconfiguration can lead to an instance being exposed to the internet without proper authentication—effectively giving an attacker a path to the same high‑privilege access the agent has (files, sessions, and tools), turning a useful assistant into a fast route to data leakage or account compromise.

    How Does OpenClaw Compare to Sentienta?

    Sentienta is a cloud-based multi-agent platform built for business workflows, where the “unit of work” isn’t a single assistant in a single thread, but a team of agents collaborating in a shared dialog. In practice, that means you can assign clear roles (research, analysis, writing, checking, ops), keep everyone grounded in the same context, and run repeatable workflows without turning day-to-day operations into an engineering project.

    It’s worth emphasizing that OpenClaw and Sentienta are aligned on a key idea: multi-agent collaboration is where real leverage shows up. Both approaches lean into specialization: having distinct agents act as a researcher, analyst, reviewer, or operator, because it’s a practical way to improve quality, catch mistakes earlier, and produce outputs that hold up better under real business constraints.

    Where they differ is less about “who has the better idea” and more about how that idea is operationalized:

    Where agents run: OpenClaw commonly runs agents on the desktop, close to local apps and local context. Sentienta agents run in the cloud, which changes the default boundary: when local data is involved, it’s typically handled through explicit user upload (rather than agents broadly operating across a machine by default).

    Time-to-value: OpenClaw is naturally attractive to builders who want maximum flexibility and are comfortable iterating on tooling. Sentienta is designed to get business teams to a working baseline quickly: Quick Start is meant to spin up a functional team of agents in seconds, with minimal developer setup for typical use.

    Collaboration model: Sentienta’s multi-agent orchestration is native to the platform: agents collaborate as a team in the same dialog with roles and review loops designed in from the start. OpenClaw can orchestrate multiple agents as well, but its ecosystem often relies on add-ons and surrounding layers for how agents “meet,” coordinate, and share context at scale.

    Net: OpenClaw highlights what’s possible when desktop agents and open ecosystems move fast; Sentienta focuses on making multi-agent work repeatable, approachable, and business-ready, without losing the benefits that made multi-agent collaboration compelling in the first place.

    Conclusion

    The bigger takeaway here is that we’re leaving the era of “one prompt, one model, one answer” and entering a world where teams of agents do the work: specialists that can research, execute, review, and refine together. OpenClaw is an exciting proof point for that future—especially for developers who want maximum flexibility and don’t mind owning the operational details that come with desktop-level capability.

    For business teams, the decision is less about ideology and more about fit. If you need rapid experimentation, deep local-machine reach, and you have the security maturity to sandbox, vet plugins, and continuously monitor an open ecosystem, OpenClaw can be a powerful choice. If you need multi-agent collaboration that’s designed to be repeatable, approachable, and governed by default—with agents running in the cloud and local data crossing the boundary only when a user explicitly provides it—Sentienta is built for that operating model.

    Either way, the direction is clear: AI is moving from standalone assistants to operational systems of collaborating agents. The right platform is the one that matches your needs for simplicity, security, and power—not just in a demo, but in the way your team will run it every day.

  • 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.

  • 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.

  • 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.

  • Managing Portfolio Workflows with NL: A Multi-Agent Use Case

    In our previous post, we demonstrated how a simple workflow could be defined and executed using plain language. This time, we examine a more advanced scenario that includes conditional branching, parallel tasks, and multiple agents interacting across different stages of the workflow. The example: a portfolio review where the Angie agent gathers real-time stock quotes, Rob analyzes price changes, and internal language models classify results. If a stock rises more than 1%, Alec sends an alert while Angie retrieves relevant news and sentiment. All of this is orchestrated by David, who builds and executes the workflow from the user’s natural language instruction. This illustrates how complex, multi-stage business logic can be expressed and enacted entirely through natural language.

    Scenario Description

    The user begins with a portfolio containing four stocks: AMZN, NVDA, AAPL, and GOOG. They ask the system to monitor each stock and take specific actions based on market performance. Specifically, if any stock in the portfolio increases in price by more than 1%, the system should both notify the user and retrieve recent news and sentiment related to that stock. Here is the user’s query:

    Here is our portfolio [AMZN, NVDA, AAPL, GOOG]. For each stock, if the price increased by more than 1%, send an alert and get the latest news and sentiment about the stock

    This scenario involves multiple agents working together: Angie collects the initial quotes, Rob performs the price analysis, and language models classify the results. If a stock meets the threshold, Alec sends an alert and Angie independently gathers news and sentiment. The system intelligently branches execution based on conditional logic and handles parallel tasks across agents—all initiated by a single natural language instruction.

    Workflow Construction Using Natural Language

    Unlike traditional no-code platforms that require dragging blocks or manually assigning agents to predefined tasks, David streamlines the entire process through natural language alone. When the user submits a request, David interprets the intent, identifies the required skills, and matches them to available agents – either defined locally or selected from the Agent Marketplace. For instance, in this scenario, David determines that Angie retrieves quotes and news, Rob analyzes the data, and Alec sends alerts. Task definitions, conditional logic, and parallel execution paths are all inferred—no manual structure or assignment required.

    As the workflow runs, the system transparently relays progress via live status updates for each completed step. At any time, the user can request a graphical representation of the active workflow. First the user gets a list of the workflows, then selects the one they want to see.

    In the detail window, the user can see the workflow, displaying all of the tasks and agents assignments:

    Once the workflow completes, users can retrieve structured results or clean up completed tasks—again with plain language:

    This abbreviated transcript shows the results of each of the tasks that the workflow agent defined and assigned. In addition to getting workflow graphs and results, the user can do the usual workflow and task management functions with natural language queries.

    Conclusion

    This scenario illustrates how complex multi-agent workflows can be created, monitored, and managed using nothing more than natural language. From interpreting conditional logic to orchestrating parallel tasks and assigning agents automatically, David abstracts away all manual configuration. Real-time execution updates, workflow visualization, and result retrieval further demonstrate the depth of control available through natural interaction. By enabling users to express business logic directly in language, Sentienta’s no-code model significantly lowers the barrier to sophisticated workflow automation.

  • How Sentienta Teams Navigate Supply Chain Disruptions: A Midwest Fulfillment Crisis

    Introduction

    When an unexpected promo surge strains Midwest operations with forecasting overshoot, logistics bottlenecks, and perilously low inventory a Sentienta Supply Chain team can help strategize solutions. In this post, we walk you through real-time data snapshots and individual agent analyses to show how distributed cognition transforms isolated insights into a unified, adaptive strategy that resolves complex fulfillment challenges.

    The Supply Chain team consists of three specialized agents who think like experts and collaborate like a team.

    • Miles: the Demand Forecaster, identifies unexpected sales surges and recalibrates forecasts to match emergent buying patterns.
    • Isla: the Inventory Optimization Strategist, spots stockout risks and reshuffles resources across distribution centers to sustain availability.
    • Talia: the Logistics Flow Strategist, detects fulfillment bottlenecks and reroutes shipments to maintain throughput and cost-efficiency.

    Each agent works from their own specialized dashboard—focused on demand, inventory, or logistics—to identify emerging risks. Once surfaced, these distinct insights are shared across the team, enabling a coordinated strategy that addresses the full scope of the disruption.

    The Data

    Isla’s Inventory Dashboard:

    Key Insight: DC-B (South) shows a 100% inventory variance with zero actual inventory and delayed container status due to port congestion.

    Miles’ Demand Dashboard:

    Key Insight: Midwest region experienced a 28% sales spike driven by influencer uplift and online channel deviation—outpacing model expectations by a wide margin.

    Talia’s Logistics Dashboard:

    Key Insight: The Midwest region shows major logistics disruption: a 59% delivery delay, 35% staffing gap at the Chicago hub, and a $1.28 per-unit cost surge—triggered by reroutes and carrier delays.

    Agent Insights – What the Dashboards Revealed to the Agents

    As part of the daily review cycle, each agent initiated a rapid diagnostic scan of their functional dashboards—surfacing anomalies, shortfalls, and emerging threats from the day’s incoming data load. The folllowing transcript captures the collaborative intake phase, where agent specialists flag critical issues in preparation for joint strategy formation. Their early assessments below form the baseline for downstream coordination.

    Supply Chain Team Transcript: Agent Analysis

    Orchestration and Strategy – When Agents Teams Work Together

    After reviewing their functional dashboards, the Supply Chain agents transitioned from isolated diagnostics to integrated strategy formation. What follows is a transcript—condensed for clarity—that reveals how a distributed team of AI experts negotiated trade-offs, merged perspectives, and built a coordinated mitigation strategy for escalating Midwest risks.

    Supply Chain Team Transcript: Team Analysis

    The team co-developed a gated intake triage plan for DC-C with four synchronized filters: SKU velocity tier, forecast lock window, supply/demand thresholds, and margin-volatility pairing. They agreed to data handoffs via shared APIs and established cap tolerances to maintain flexibility under risk. This interaction exemplifies emergent cognition—where no individual agent held the entire solution, but collaboration yielded a coherent, executable plan.

    Conclusion

    This example highlights Sentienta’s core advantage: turning fragmented functional data into synchronized decision intelligence. As agents negotiate thresholds, define roles, and operationalize shared triggers, strategy becomes not just automated—but emergent. Sentienta Teams adapts at the pace of complexity, enabling businesses to respond with coordinated precision.

  • Your Team Is Using AI Wrong—But Not For the Reason You Think

    Nate Jones is one of the sharpest observers of the AI industry, offering thoughtful takes on how the field is evolving faster than most teams can adapt. In one of his recent posts (and video), he highlights a crucial, yet often overlooked insight: new knowledge doesn’t just come from tools. It emerges from how teams think together.

    He’s absolutely right. But while others are still figuring out how to retrofit ChatGPT into legacy workflows, we built something different from the start.

    Sentienta wasn’t built to join a team—it was built to be one. An architecture where cognition is emergent, shared, and preserved through expert agent interaction.

    This post is our view on Nate’s insight about ‘distributed cognition’ and a demonstration of what it looks like in action.

    What Is Distributed Cognition?

    In traditional systems, intelligence is seen as residing in individuals or in the outputs of standalone tools. But real team intelligence is different. It’s a dynamic process: understanding emerges as people, and now agents, interact, adapt, and build on one another’s contributions.

    Sentienta is designed around this principle. Its expert agents don’t just complete tasks, they participate in a continuous, evolving exchange of reasoning. Each brings a domain-specific perspective, and through ongoing dialog, they generate insights that no single agent—or human could reach alone.

    This isn’t just “stored knowledge”—it’s active cognition. When agents respond to one another, challenge assumptions, and adapt strategies together, they form a cognitive system. What emerges isn’t data, but collective understanding.

    Sentienta isn’t a system for remembering what happened—it’s a system for thinking together in real time.

    This is what makes Sentienta more than a workflow tool. It is distributed cognition embodied: an always-on, always-evolving team of minds—virtual and human, each contributing to a deeper, shared sense of what’s true and what to do next.

    Innovating Through Collaborative Insight

    The following graphic shows how a Pricing Strategist’s initial idea evolves through critical input from a Customer Behavior Analyst into a novel “build-your-own bundle”, the visualization highlights Sentienta’s ability to generate breakthrough strategies through collaborative agent interactions.

    What begins as expert input becomes something more—a new idea born through structured interaction. Sentienta not only facilitates this dynamic exchange but preserves the conversation, making insight traceable, reusable, and ready for replay when it matters most.

    Emergent Strategy from Agent Teamwork

    Teamwork is essential because it drives creative problem solving on multiple levels: humans contribute diverse perspectives and strategic intuition, while agents rapidly process data and combine insights at scale. This dual approach means that by integrating people with high-performing agent teams, businesses can overcome the natural limits of human capacity, ensuring that expertise expands without additional headcount.

    Sentienta’s platform not only leverages this synergy by preserving collaborative dialogs to build a lasting archive of insights but also serves as a dynamic space for co-creating new ideas through agent collaboration. By surfacing insights that no single agent or person could produce alone, Sentienta teams exemplify emergent cognition-delivering strategies born from structured, multi-perspective dialog.

  • From Signals to Strategy

    How AI Agent Teams Transform Decision-Making

    In today’s real-time economy, with almost daily turmoil, waiting for static dashboards or end-of-day reports puts businesses at risk. Decision-makers need faster insight and smarter responses—and they’re finding both in AI-powered agent teams. This post follows a “decision-day” walk-through where scheduled agents detect anomalies, trigger analysis, and deliver actionable intelligence. Along the way, we’ll preview a powerful future: dynamic response teams that self-assemble on demand, transforming routine data monitoring into collaborative, AI-driven strategy.

    From Manual Monitoring to Team Workflows

    For years, business teams relied on dashboards and manual checks—forcing analysts to sift through data after the fact, often too late to pivot strategy. AI agents like Angie now automate this process, persistently scanning KPIs and surfacing anomalies in near real time – bringing signal to the surface before teams even ask. This shift transforms monitoring from a reactive task into a proactive loop, enabling tactical adjustments as performance fluctuates throughout the day.

    Monitoring TypeDescriptionResponse SpeedHuman Effort
    Manual MonitoringAnalysts check KPIs periodically; issues often spotted lateSlowHigh
    Scheduled TriggersAgents run on set schedules to check and report KPIsModerateMedium
    Team Workflows (Emerging)Agent teams collaborate in sequence—detecting, diagnosing, and acting as events unfold in real timeFastLow

    Early adopters gain more than speed – they free up analysts from routine checking, letting them focus on root cause, strategic options, and next steps.

    Intelligent Agent Teams and Semi-Autonomous Coordination (Today)

    Sentienta Teams lets you assemble expert AI agents customizable to your business context – think of them as always-on teammates with specialized roles. With data collected continuously by Angie, users can periodically request updates, triggering collaborative evaluation across the team. Insights emerge through the interplay of domain-specific agents, each contributing to problem-solving like part of a cohesive virtual task force.

    What’s powerful today is the modular collaboration between agents: Angie spots the signal, users prompt an update, and the team responds—analyzing root causes, surfacing implications, and aligning recommendations. This tiered interaction shortens feedback loops while keeping the human-in-the-loop where needed.

    This model already elevates decision quality by transforming raw signal detection into team-powered insight—without code or integration overhead. Let’s see how this might work:

    Scenario Walk-Through — “End-of-Quarter Chain Reaction”

    At 9:55 AM, Angie, the KPI Monitoring Agent, completes her scheduled data sweep: collecting conversion metrics, analyzing trends against prior cycles, and logging a 12% drop. She also factors in data from the UX Deployment Team’s new UI update logged at 8:00 PM yesterday, establishing a preliminary correlation with increased mobile bounce rates.

    9:55 AM: Angie (KPI Monitoring Agent): “Scheduled Insight Logged — Conversion rate down 12% vs prior cycle. Ingested UX deployment log from 8:00 PM yesterday—preliminary correlation with mobile exit rates established.”

    At 10:05 AM, CMO Alex Morgan reviews Angie’s logged summary while preparing for the QBR, triggering further analysis from the broader agent team.

    10:05 AM: Analytics Agent: “Manual review confirmed. Running historical comparison — dip predominantly isolated to mobile traffic sources.”

    10:07 AM: Competitive Intelligence Agent: “Scanning competitor trends… Detected concurrent promotional push by primary rival.”

    10:10 AM: Summary Agent: “Final insights compiled: Traffic dip largely traced to mobile UX issues with the recent UI update, compounded by competitor campaign. QBR-ready report delivered to shared location.”

    10:12 AM: Presentation Update Agent: “Integrating the final report into the QBR presentation—slides refreshed with updated KPI graphs and attribution notes reflecting the new UI deployment impact.”

    This scenario illustrates how scheduled data logging, combined with cross-departmental inputs and manual review, triggers a coordinated AI response that supports executive decision-making.

    Next-Gen Workflow Automation: Intelligent Sequences & Self-Assembling Expertise

    Get ready for a system where a planning agent can automatically orchestrate the entire process: sequencing actions, branching based on real-time data, and repeating steps as needed. As soon as anomalies are detected, it will dynamically self-assemble the precise mix of expert agents to diagnose issues, forecast impacts, and execute tailored responses, shifting reactive monitoring into a proactive, intelligent strategy.

    This next-generation automation doesn’t just detect an issue—it scripts the solution by activating specialized agents at each step, adapting in real time as conditions evolve.

    Each automated workflow acts like a living blueprint, employing branching logic, conditional triggers, and intelligent retries to ensure the right path is taken as new data emerges.

    From manual monitoring to intelligent workflows, and now toward dynamic, self-assembling response teams, Sentienta is redefining how decisions are made. These AI-powered teams don’t just react, they anticipate, adapt, and execute strategies in real time.

  • Powering Productivity: Task Scheduling for Intelligent Agents

    In previous posts, we’ve explored agent autonomy—how intelligent agents can work independently to accomplish complex goals. Today, we’re unlocking a new level of autonomy: the ability to schedule agents and teams to run tasks automatically at specific times. With task scheduling, agents can operate in the background—gathering information, performing analysis, and delivering results—without your ongoing involvement.

    Some tasks require regular execution over time, especially those that track changeable data. For example, say you’re monitoring market sentiment for a specific stock. Instead of having to prompt Angie, the Web Search Agent, to gather updates every hour, you can now schedule her to collect that data and compile a report without manual input.

    Why is Scheduling Important?

    This is also ideal for larger, resource-intensive tasks—such as background research, document synthesis, or team workflows—that benefit from being handled asynchronously. You can stay focused on other priorities while your agents work behind the scenes.

    How Does this Work?

    The Angie Web Search Agent (available in the Agent Marketplace under Document and Content Access) now supports temporal scheduling. Simply configure a time or interval and specify what you want the agent or team to accomplish. Sentienta handles task execution and ensures you receive a detailed summary when the job is done.

    This capability isn’t limited to a single agent. You can coordinate multiple agents or entire teams to run collaboratively or in parallel on timed schedules, enabling sophisticated workflows that unfold automatically over time. It’s automation at scale—with intelligence.

    To see how this works, consider the following interaction:

    User: Schedule a task to run every 10 minutes for 6 runs. The task is to query “What is the latest AMZN quote?”

    Wed, 09 Apr 2025 17:50:10 Angie:

    Task user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule is scheduled to run every 10 minutes and will stop after 6 runs.

    The agent has created a scheduled task to search for the latest Amazon.com stock quote every 10 minutes. This will run 6 time and then complete.

    You can get a list of all the scheduled tasks and their status with the following:

    User: List scheduled tasks

    Wed, 09 Apr 2025 17:56:56 Angie:

    user-schedule-clscofield997-at-gmail.com-AAPLquoteschedule complete

    user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule running

    user-schedule-clscofield997-at-gmail.com-NVDAquoteschedule cancelled

    Angie will list each of the tasks that have been scheduled and their status. At any time you can get the latest results from a task with:

    User: Get results for task user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule

    Wed, 09 Apr 2025 18:01:05 Angie:

    Here are the current results for user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule:

    Wed, 09 Apr 2025 17:50:38 Angie: As of today, Wednesday, April 9, 2025, here is the latest information on Amazon’s stock (AMZN):

    – Current Price (Last Trade): $184.14

    – Change from Previous Close: +$13.48

    – Percentage Change: +7.90%

    – Opening Price: $172.12

    – Day’s High: $186.22

    – Day’s Low: $169.93

    – Previous Close: $170.66

    Amazon’s stock has seen a strong gain today, rising nearly 8%, indicating strong investor interest or positive market news affecting the company.

    When the task has completed, Angie will take all of the intermediate results and compile a report. If there are numerical values (like stock prices), she’ll compile these into a table:

    Here is a consolidated table summarizing the key stock metrics for Amazon (AMZN) on April 9, 2025, based on the multiple time-stamped reports:

    MetricValue(s) Reported
    Current Price$187.24, $186.67, $186.68, $187.56, $187.59
    Price Change+$16.58, +$16.01, +$16.02, +$16.90, +$16.93
    Percent Change+9.72%, +9.38%, +9.39%, +9.90%, +9.92%
    Opening Price$172.12 (Consistent across all reports)
    Day’s High$187.92 (4 reports), $187.98 (latest report)
    Day’s Low$169.93 (Consistent across all reports)
    Previous Close$170.66 (Consistent across all reports)

    📝 Summary of Research Findings:

    On April 9, 2025, Amazon’s (AMZN) stock experienced a substantial surge, consistently reported as a ~9.4–9.9% increase from the previous closing price of $170.66. The opening price for the day was $172.12, and the lowest traded price was $169.93. Throughout the day, the high varied slightly between reports, peaking between $187.92 and $187.98.

    The final snapshot reported a trading price of $187.59, reflecting a daily gain of $16.93 or 9.92%. The consistency across key metrics such as opening, low, and previous close enhances confidence in these results.

    Overall, the stock showed strong upward momentum likely driven by significant investor sentiment or positive news, marking one of the most bullish single-day movements for Amazon in recent times.

    Of course Angie will also perform all the necessary task management duties too. For example, you can cancel a task at any time:

    User: Cancel task user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule running

    Wed, 09 Apr 2025 18:12:55 Angie:

    The task user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule will be cancelled.

    And you can delete the results from former tasks with:

    User: Delete results for task user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule

    Wed, 09 Apr 2025 18:16:29 Angie:

    The task results for user-schedule-clscofield997-at-gmail.com-AMZNquoteschedule were deleted.

    Beyond Reporting: Orchestrating Workflows with Scheduled Intelligence

    Beyond data gathering, scheduled tasks can drive entire workflows—triggering follow-up actions, coordinating hand-offs between agents, or summarizing findings across time. For example, after collecting market data, a second agent might be scheduled to generate trend reports or highlight unusual activity. This kind of orchestration transforms agents from simple tools into participants in a broader, ongoing process that adapts to your needs.

    In short, Sentienta’s task scheduling unlocks a new depth of autonomy and responsiveness. By setting your agents on a timeline, you gain continuity, consistency, and clarity—without losing control. Whether you’re monitoring real-time events or streamlining internal analysis, this feature helps you keep pace with the demands of modern work through intelligent automation.

  • Adding Real-time Data to Your Agent Dialogs

    One of the biggest challenges with LLMs is their limited access to new information. These massive models take months to train, followed by additional months of testing and refining safeguards to protect users from harmful content and unauthorized queries. However, for LLM-based agents to truly be effective, they must have access to the most up-to-date data.

    I’ve been exploring Sentienta’s latest real-time search agent, ‘Angie,’ available in the Agent Marketplace under the ‘Document and Content Access’ section. Angie offers unique capabilities, including real-time news searches, detailed financial report summaries—covering SEC filings, stock news, sentiment analysis, and price history—and the ability to summarize content from open web pages.

    Sentienta’s Angie agent offers these capabilities without the need for coding or integration, unlike OpenAI and Google’s Gemini platforms. It provides a no-code solution for seamlessly incorporating real-time data into team discussions.

    Under the covers, this agent makes use of an integrated tool suite, intelligently selecting the best tool for the task. For content searches there are tools that directly integrate third-party APIs. This ensures that you don’t have to manage API keys and subscriptions. Instead you get the most up-to-date content when you need it. It just works.

    Here are some examples of how you can use Angie in your teams. Suppose you are interested in the latest SEC filings on Uber. I can’t reproduce the full result here, but here is an abbreviated transcript:

    Me: Angie get the latest sec filings for Uber

    Thu, 20 Mar 2025 17:59:26 Angie:

    I will get a report on UBER, from document 10-K

    Thu, 20 Mar 2025 17:59:27 Angie:

    This document has 142 pages.

    Thu, 20 Mar 2025 18:00:25 Angie:

    The document is a 10-K annual report filed by Uber Technologies, Inc. with the SEC for the fiscal year ending December 31, 2024. It contains comprehensive financial and operational details about the company and its activities.

    … (multiple pages of analysis)

    The document also discusses market risks, including interest rate risk, investment risk, and foreign currency risk. It details the company’s investment policies aimed at capital preservation and mitigating risks associated with investments in publicly traded and privately held entities. Foreign currency fluctuations pose a risk to international revenues and expenses, partially offset by incurring expenses in the same foreign currencies. The company uses derivative contracts to manage currency risks but acknowledges that these measures cannot fully eliminate exposure.

    After reading this summary of the 10-K, perhaps you’d like to see the latest news on Uber:

    Me: Angie what is the latest news on Uber?

    Thu, 20 Mar 2025 18:45:29 Angie:

    Here is the best possible answer based on the search results for the latest news on Uber as of Thursday, March 20, 2025:

    Uber Technologies (NYSE: UBER) has been performing strongly in 2025, with its stock up 31.55% year-to-date. Analysts have set a consensus price target of $90.7, indicating a potential 14.16% upside. Uber has also announced a $1.5 billion accelerated share repurchase program as part of its broader $7.0 billion repurchase authorization, showing confidence in its financial position. Additionally, for Q1 2025, Uber expects bookings to grow between 17%-21%, indicating robust operational performance going forward.

    [5 supporting links]

    With that in hand, perhaps you’d like to see the latest closing price for this stock:

    Me: Show me the latest closing price for Uber’s stock

    Thu, 20 Mar 2025 18:53:16 Angie:

    As of the most recent data refresh on March 19, 2025, at 19:55 EST, Uber Technologies Inc. (UBER) had the following stock performance:

    • – Last traded price (close): $73.39
    • – Opening price: $73.345
    • – Daily high: $73.39
    • – Daily low: $73.3001
    • – Trading volume: 5,430 shares

    This data is based on intraday trading with 5-minute intervals. If you need more details such as historical trends or analyst insights, please let me know!

    There are of course, many ways to access this data, including a series of Google searches. However, Angie automatically integrates it into your team’s dialog, making it readily available to other agents. My team included a Research Lead, ‘Roger,’ who analyzed Angie’s results. Below is a brief transcript of Roger’s suggested next steps.

    Me: Roger – please summarize what we’ve learned about Uber. Is this a good investment?

    Thu, 20 Mar 2025 18:57:06 Roger:

    Uber has gained 31.55% year-to-date, with analysts targeting $90.7, indicating a 14.16% potential upside. A $1.5 billion share repurchase suggests financial confidence, and 17%-21% bookings growth signals strong operations. The latest closing price was $73.39 as of March 19, 2025. While the outlook appears positive, further analysis of risks, competition, and market conditions is recommended before making an investment decision.

    Me: What does this data tell you about risks?

    Thu, 20 Mar 2025 18:58:43 Roger:

    Uber’s strong year-to-date growth and share repurchase suggest confidence, but risks remain. Potential concerns include market volatility, regulatory challenges, competition from Lyft and autonomous vehicle companies, and macroeconomic factors affecting ride demand. Further analysis of revenue stability and profitability trends is necessary.

    By exploring competitive trends and regulatory changes, I gained additional insights, too many to show here. This demonstrates the power of Angie as a research assistant and the strength of a team that can analyze data to highlight what truly matters. In a future post, I’ll showcase how the Research Lead delegates research tasks to Angie and compiles an investment opportunities report—without writing a single line of code.