Sentienta Blog

Tag: llm

  • 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.

  • A Deep-dive into Agents: Tool Access

    An important feature of agents is their ability to utilize tools. Of course there are many examples of software components that use tools as part of their function, but what distinguishes agents is their ability to reason about when to use a tool, which tool to use and how to utilize the results.

    In this context, a ‘tool’ refers to a software component designed to execute specific functions upon an agent’s request. This broad definition includes utilities such as file content readers, web search engines, and text-to-image generators, each offering capabilities that agents can utilize in responding to queries from users or other agents.

    Sentienta agents can access tools through several mechanisms. The first is when an agent has been pre-configured with a specific set of tools. Several agents in the Agent Marketplace utilize special tools in their roles. For example, the Document Specialist agent (‘Ed’) which you can find in the Document and Content Access section, utilizes Amazon’s S3 to store and read files, tailoring its knowledge to the content you provide.

    Angie, another agent in the Document and Content Access category, enhances team discussions by using a search engine to fetch the latest web results. This is valuable for incorporating the most current data into a team dialog, addressing the typical limitation of LLMs, which lack up-to-the-minute information in their training sets.

    You have the flexibility to go beyond pre-built tools. Another option allows you to create custom tools or integrate third-party ones. If the tool you want to use exposes a REST API that processes structured queries, you can create an agent to call the API (see the FAQ page for more information). Agent ‘Ed’, mentioned earlier, employs such an API for managing files.

    Finally, Sentienta supports completely custom agents that embody their own tool use. You might utilize a popular agent framework such as LangChain, to orchestrate more complex functions and workflows. Exposing an API in the form we just discussed will let you integrate this more complex tool-use into your team. Check out the Developers page to see how you can build a basic agent in AWS Lambda. This agent doesn’t do much, but you can see how you might add specialized functions to augment your team’s capabilities.

    In each case, the power of agent tool-use comes from the agent deciding how to use the tool and how to integrate the tool’s results into the team’s dialog. Agents may be instructed by their team to use these tools, or they may decide alone when or if to use a tool.

    This too is a large subject, and much has been written by others on this topic (see for example here and here). We’ve touched on three mechanisms you can use in Sentienta to augment the power of your agents and teams.

    In a future post we’ll discuss how agents interact in teams and how you can control their interactions through tailored personas.