Sentienta Blog

Tag: AI collaboration

  • A Deep-dive into Agents: Agent Autonomy

    A Deep-dive into Agents: Agent Autonomy

    In past posts, we’ve explored key aspects of AI agents, including agent memory, tool access, and delegation. Today, we’ll focus on how agents can operate autonomously in the “digital wild” and clarify the distinction between delegation and autonomy.

    Understanding Delegation and Autonomy

    Agent delegation involves assigning a specific task to an agent, often with explicit instructions. In contrast, autonomy refers to agents that operate independently, making decisions without significant oversight.

    Within Sentienta, agents function as collaborative experts, striking a balance between autonomy and delegation for structured yet dynamic problem-solving. Autonomous behavior includes analyzing data, debating strategies, and making decisions without user intervention, while delegated tasks ensure precise execution of specific actions.

    For example, a Business Strategy Team could autonomously assess market trends, identify risks, and refine strategies based on live data. At the same time, these agents might delegate the task of gathering fresh market data to a Web Search Agent, demonstrating how autonomy and delegation complement each other.

    Extending Autonomy Beyond Internal Systems

    Sentienta Assistant agents and teams can also function beyond internal environments, operating autonomously on third-party platforms. Whether embedded as intelligent assistants or collaborating in external workflows, these agents dynamically adapt by responding to queries, analyzing evolving data, and refining recommendations—all without requiring continuous oversight.

    Practical Applications of Autonomous Agents

    Below are practical applications showcasing how agents can operate independently or in collaboration to optimize workflows and decision-making.

    • Financial Advisory & Portfolio Management (Single Agent) A financial advisor agent reviews portfolios, suggests adjustments based on market trends, and provides personalized investment strategies.
    • Customer Support Enhancement (Single Agent or Team) A support agent answers queries while a team collaborates to resolve complex issues, escalating cases to specialized agents for billing or troubleshooting.
    • Data-Driven Market Research (Sentienta Team) A multi-agent team tracks competitor activity, gathers insights, and generates real-time market summaries, using delegation for data collection.
    • Legal Document Analysis & Compliance Checks (Single Agent) A legal agent reviews contracts, identifies risk clauses, and ensures regulatory compliance, assisting legal teams with due diligence.
    • Healthcare Support & Patient Triage (Single Agent) A virtual medical assistant assesses symptoms, provides diagnostic insights, and directs patients to appropriate specialists.

    The Future of AI Autonomy in Business

    By combining autonomy with effective delegation, Sentienta agents serve as dynamic problem-solvers across industries. Whether streamlining internal workflows or enhancing real-time decision-making, these AI-driven assistants unlock new possibilities for efficiency, expertise, and scalable innovation.

  • A Deep-dive into Agents: Agent Delegation

    In my last post, we explored how agents interact within a team. Building on that foundation, let’s examine agent delegation—a structured process in which agents assign tasks to others based on expertise, priority, and context.

    Unlike agent autonomy, which I’ll cover in a future post, agent delegation focuses on deliberate, workflow-driven collaboration among agents. Rather than acting independently, agents make informed decisions about which tasks they should handle and which should be handed off to specialized counterparts.

    Structuring Delegation in Agent Teams

    Sentienta agents operate based on personas—natural language descriptions of their expertise. These personas guide how an agent engages in problem-solving within a team. Crucially, each agent has awareness of its teammates’ expertise, as this information is embedded in the system prompt of their respective language models.

    When responding to a query, each agent evaluates both its own capabilities and how best to leverage the expertise of others. This adaptive delegation is an essential feature of Sentienta’s design. Agents iteratively work through problems, sharing insights, refining their contributions, and identifying gaps in the discussion. When an agent determines that a particular aspect of a query requires specialized attention, it can delegate the task—often providing specific instructions on how to approach it.

    This structured, dynamic handoff is what differentiates agent delegation from the broader concept of agent autonomy. While autonomy involves independent decision-making, delegation is about intelligent collaboration.

    A Practical Example: Agent-Driven Financial Analysis

    To illustrate, let’s consider a small Sentienta team analyzing financial markets. This team consists of:

    • 🔹 Financial Analyst Agent – Interprets market data, economic trends, and financial reports.
    • 🔹 Risk Assessment Agent – Evaluates market volatility, credit ratings, geopolitical risks, and sector stability.
    • 🔹 Web Research Agent – Gathers external data, such as stock performance, news reports, and regulatory changes.

    A delegated workflow might operate as follows:

    1. Financial Analyst Agent requests the Web Research Agent to gather financial reports and market performance data.
    2. Risk Assessment Agent instructs the Web Research Agent to track real-time market volatility and news on macroeconomic risks.
    3. Web Research Agent retrieves and summarizes relevant data, providing source links for deeper analysis.
    4. Financial Analyst Agent selects key companies for further investigation and delegates risk-factor analysis to the Risk Assessment Agent, requesting a review of leadership stability, credit ratings, and sector trends.
    5. If complex statistical trends emerge, an additional Data Analytics Agent might be introduced to identify patterns and forecast future performance.

    Crucially, these steps are not static. The delegation process evolves dynamically, responding to new information in real time.

    The Benefits of Task Delegation

    By structuring delegation in this way, Sentienta teams achieve modular adaptability—scaling efficiently as new agents are introduced or refined without burdening a single model. This approach ensures that specialized tasks are handled by the most relevant agents, improving both accuracy and depth of analysis.

    But what happens when agents move beyond structured delegation toward autonomous strategic decision-making? In a future post, I’ll explore how Agent Autonomy is set to redefine enterprise AI, reducing human intervention while maintaining control and reliability.

  • A Deep-dive into Agents: Agent Interaction

    In previous posts, I introduced the capabilities of individual agents, including their unique memory architecture and tool access. But what sets Sentienta apart is its multi-agent platform, where agents work together to solve problems. Today, we’ll explore how these agents interact as a team.

    How Agents Work Together

    Let’s consider an example: imagine you’ve formed a team to design a new electric scooter. We’ll call this the Scooter Team, and it’s type is Product Design.

    The team consists of key specialists: a VP of Product Design as team lead, a mechanical engineer with expertise in two-wheeled vehicles, an electrical engineer for the scooter’s power system, and a legal representative to ensure compliance with regulations. In future posts, we’ll discuss how to create specialist agents in Sentienta, but for now, imagine they’re in place and ready to collaborate.

    Once the team is set up, you initiate a discussion—say, “Let’s consider all the elements needed in the scooter design.” Each agent processes the request from its area of expertise and contributes insights. As they respond, their inputs become part of an ongoing team dialogue, which, as discussed in this post, is stored in each agent’s memory and informs subsequent responses.

    Iterative Problem-Solving

    Agents interact much like human working groups: they listen to teammates before responding, integrating their insights into their own reasoning. This iterative exchange continues until the original question is thoroughly addressed.

    What does that mean for the scooter design team? Suppose that the first response comes from the mechanical engineer: she tells the team about the basic components in the design, and in particular suggests the power that is needed to drive the scooter. The electrical engineer will consider this power specification when developing his response. The agent representing legal may note that regulations cap the scooter’s speed at 25 mph.

    And this is what is interesting: the input from legal may cause the mechanical and electrical engineers to reconsider their answers and respond again. This iterative answering will continue until each agent has contributed sufficiently to fully address the query. Reasoning about the user’s question derives from this agent interaction.

    The Role of LLMs in Agent Interaction

    How does this happen? The engine that drives each agent is an LLM. The LLM system prompt includes several key instructions and information that foster team interaction: the team definition along with teammate personas are included in the prompt enabling the LLM to consider who on the team is best able to address aspects of the query.

    In addition, each agent is instructed to critically think about input from teammates when developing a response. This makes team dialogs interactive and not just isolated LLM responses. Agents are instructed to consider whether the query has been answered by other agents already. This helps to drive the dialog to conclusion.

    Looking Ahead

    This dynamic interaction forms the foundation of Sentienta’s multi-agent problem-solving. In future posts, we’ll explore concepts like delegation and agent autonomy, further uncovering the depth and efficiency of these collaborations.

  • Teams, Tasks, Tales

    I was given early access to Sentienta to test out features and work through bugs. I found that Sentienta had both fun and helpful applications. Now that Sentienta is released I wanted to give new users ideas for things that they can try based on what worked really well for me.

    Human Resources Team

    I manage a second job and sometimes need to create material for my work. At one point, I discovered an area at my workplace that lacked a specific policy. I created a Sentienta HR team of experts, including an HR staff member, a policy writer, workplace stakeholders, a consumer advocate, a proxy for an attorney, and a risk manager. This group served as a sounding board and consultation resource to draft the new policy.

    tip Tip:

    To do this, I first created the team in the Manage Teams tab, providing a name for the team, a title and brief description. Then I developed some agents for it, with the most important part of each agent being it’s persona. The persona of an agent focuses that agent’s contributions in a dialog to a specific area of expertise. Here is an example of what the persona might be for a consumer advocate:

    “You are an advocate and voice for consumers, helping to resolve complaints and ensuring fair practices in business transactions. Additionally, you may engage in public policy efforts to promote transparency and accountability, aiming to improve consumer protection laws and regulations.”

    Note that the persona is drafted to read like instructions to the agent to help focus its contributions. Not sure how to write the description? You can always Google it and edit it down, and the persona can be adjusted over time.

    It was interesting seeing the agents respond to each other and give feedback on each other’s inputs. Each member contributed to the dialog, offering new ideas and suggestions. The dialog concluded once each agent had an opportunity to participate and move the conversation to a solution.

    Since forming this team, I’ve used it to discuss general policies and related topics whenever I have concerns or ideas. As this is my second job, I am not an expert in many areas, and my focus is just a small part of it. Nonetheless, this team has been instrumental in keeping me informed.

    I want to be clear that I did not provide the team with any specific information or data that could be considered sensitive or proprietary. But even without that specific data, I’ve found the team to be a powerful resource for thinking about the broader issues in my workplace.

    Lit Review Team

    For fun, I enjoy writing, and Sentienta offers a neat capability to form a dream team of literary reviewers. I’ve created a team with iconic writers such as William Shakespeare, Ernest Hemingway, and Edgar Allan Poe. To anchor the team’s feedback in a contemporary style, I included my favorite author, Jim Butcher.

    When I provided pieces of a project I was writing, and asked the team to review it, each agent gave information based on their assigned personas. The feedback was remarkable and gave unexpected insights into my work, enhancing not only my understanding of how readers perceive my work but also deepening my connection to the passion that fuels my writing.

    tip Tip:

    Here is how you can add content to your team (in my case, writing samples): click the paperclip icon located in the toolbar on Sentienta’s main page. This will let you select a file from your desktop (most file formats are supported). Once you click ok, the file will load and you can enter a question or direction to the team for how to use the file’s contents. All of the agents will have access to file and can use it in the dialog.

    At one point I also tried having the various agents rewrite parts of my work in their own style. This gave me interesting view points and helped with the editing process.

    The team, dubbed Lit Review, also functioned as a great way to learn about the authors and better understand each author’s writing. It was fun to watch these famous writers edit each other’s responses!

    I feel obliged to add in here that I never used the team to write my project for me. I used it to give guidance and ideas for improving my writing’s quality. Professionals could easily use this to edit their own work, whether in business or creative writing.

    Sentienta has become an invaluable tool for tackling the challenges I face both professionally and creatively. The Lit Review team will remain one of my go to’s, but I also intend to form new teams to explore how they can support me.

  • Sentienta Teams

    Sentienta is different from your current experience with AI chatbots. The essence of our product is that something special comes from the interaction of experts.

    When ideas flow from one expert to another, they are evaluated, improved, verified, and sometimes debunked. The dialog between experts is a window into the evolution of ideas and solutions to problems. This transparency is the foundation of trust.

    This is why you find teams in companies: having just one person solve a problem is ok, but getting the perspective of people with different skills makes your solution much more robust.

    Our latest presentation explains how this idea can be used in your company by building virtual teams of GenAI agents.

    https://www.sentienta.ai/blog/Sentienta.pdf