LLM Agent Implementation Overview

Software systems that use large language models (LLMs) to perform tasks autonomously, often involving complex workflows, tool usage, and decision-making.

A type of agentic system that follows a predefined sequence of steps and uses LLMs to execute specific actions within those steps.

A more flexible type of agentic system that leverages LLMs for decision-making and adapting to changing conditions, allowing for a wider range of behaviors.

The process of simplifying tasks by directly interacting with LLM APIs, avoiding unnecessary abstraction layers.

Using frameworks that simplify common LLM tasks like calling APIs, defining tools, and chaining actions together.

The act of ensuring understanding of how an LLM framework operates beneath the surface, enabling effective debugging and problem-solving.

The process of starting with the simplest solution and adding complexity only when necessary, as a way to balance efficiency and performance.

The balance between the speed and cost of an LLM solution versus its task performance.

The process of refining and improving prompts to guide an LLM's behavior and achieve desired outputs.

A way to evaluate an LLM's performance on a specific task, like code completion or problem-solving. It involves comparing the LLM's outputs against expected outcomes.

External tools that allow an AI agent to interact with real-world services and APIs, expanding its capabilities.

The process of simplifying the design of an AI agent by using basic components instead of complex frameworks, especially when moving towards production.

The practice of optimizing the format of data passed between an AI agent and its tools to improve efficiency and minimize errors.

A type of LLM-based system that combines conversational abilities with the power to interact with external tools to solve problems.

The principle of only adding complexity to an AI system if it is proven to improve the results.

The ability of an AI agent to complete tasks requiring both interaction with users and external tools.

The effectiveness of an AI agent in solving real-world problems, especially those with clear success criteria and feedback loops.

A process where a task is broken down into a sequence of steps, each executed by an LLM call, using the output of the previous step.

This pattern is used when tasks can be divided into distinct categories, and each category is handled more effectively by a specialized LLM or model.

A pattern where multiple LLMs work simultaneously on different parts of a task, with their outputs combined for a final result.

A workflow where a central LLM acts as an orchestrator, breaking down tasks, delegating to worker LLMs, and integrating their results.

A workflow involving an iterative refinement process, with one LLM generating responses and another evaluating and improving them.

LLMs are augmented with capabilities like retrieval, tools, and memory, which allow them to perform actions like generate search queries, select appropriate tools and remember information.

A protocol that allows developers to integrate with a growing ecosystem of third-party tools using a simple client implementation.

A way to ensure that a prompt chaining workflow is on track by adding programmatic checks at intermediate steps.

The ability of an agent to gain information from the environment to assess progress, such as tool call results or code execution.

Points in an agent's execution where it can pause to receive human feedback, either voluntarily or when it encounters a blocker.

Situations where agents pause because they require additional information or guidance from a human.

Conditions that define when an agent should stop working, such as a maximum number of iterations or a successful completion of the task.

Testing an agent thoroughly in a controlled environment to ensure safe and reliable operation before deployment.