Agents and Retrieval Methods

An AI agent is simply a folder containing three things: instructions (a prompt or skill), a way to access tools, and a system for memory. That’s it. When you combine these and give the agent the right tools, you get reliable, repeatable output. Agents are not just demos anymore—they can complete real long-horizon work, such as writing code, running experiments, debugging failures, and producing useful results autonomously. Managing agents is like managing people; the better you communicate expectations, the better the outcome.

Retrieval methods are how the agent accesses the information it needs. You don’t actually build agents—you build the information hierarchy (a structured library of documents and definitions) that the agent reads. For example, you document your design language: “a primary button is blue and has this shape.” When an agent reads that hierarchy, it can make consistent decisions. Humans learn best when information is organized, and AI learns best the same way. You can build this structure once, point any AI at it (Claude, ChatGPT, etc.), and that AI becomes your agent.

This matters because agents now do meaningful work, not perfect work, but work that solves pieces of a problem across longer task chains. Knowing when to use an agent versus a simple deterministic workflow (where the same input always yields the same output) is essential. Agents are non-deterministic—they can handle edge cases and adapt. By building a clear information hierarchy and using retrieval methods to feed it to your agent, you create reusable intelligence that scales across any AI tool you adopt tomorrow.

To use agents (automated AI assistants that complete tasks) and retrieval methods (ways to fetch relevant information) well, start with clear instructions. An agent works best when you define its core brief—like a skill in Claude or ChatGPT—for a specific recurring task. The agent then calls the right search tools with correct parameters; too many parameters make this hard for the agent.

For retrieval, move beyond a single vector DB (database storing data as mathematical vectors) call. Use iterative retrieval, where the agent searches, learns from results, and searches again. This creates a kind of index, like a library card, that helps the agent fetch only what it needs. Hanna Lichtenberg from Mixedbread AI built an agent with four main search tools, including a wide overview search that retrieves up to 50 chunks (pieces of content). This keeps the agent precise, fast, and cheap.

A good example: if you want to search for "movies with animal superheroes," a simple search might miss results. An agent using iterative retrieval understands the intent and searches more effectively. Start with a simple single query, then layer in iterative loops as tasks grow complex. The key lesson: retrieval is no longer a one-time call—agents must understand what they search and search to understand more. Always treat the AI as your skilled assistant, and keep instructions focused.

A common mistake when building agents (AI programs that perform tasks for you) is not defining what done looks like. Agents need a clear finish line; otherwise they keep looping, overcomplicate things, or waste time on simple answers.

A useful retrieval (finding relevant information) strategy is to give the agent multiple search tools rather than committing to one approach. For example, an agent can have an overview search (a wide semantic search pulling up to 50 chunks) for broad questions, and more specific tools for exact identifier lookups versus conceptual searches across documents. This balanced set of tools gives you a low floor and high ceiling. If you don't know your agent's query behavior yet, start with a general-purpose tool, then log its behavior to refine later.

A key pitfall is using chunk-based retrieval on tabular data. If you ask “what week had the highest sales,” the agent searches for “highest sales” and may pull back irrelevant chunks, missing the actual answer. Train the agent to choose the right tool for the data type.

When you give an agent many skills, it creates another problem: choosing the right one and combining results. For instance, separate agents searching for hotels, flights, and locations may return contradictory answers. You need a harness that ensures results combine coherently.

Finally, remove contradictions, compress useful lessons, and prevent the agent from filling up with old or messy information. A specialized tool can produce an answer that looks confident but is wrong, so always check results. Best practice: iterate, log behavior, and use retrieval rewards to optimize tool choice and query quality.