An AI agent is a system that perceives its environment, reasons about what to do, takes actions, and observes the results — iterating until it achieves a goal. Designing one that is reliable, debuggable, and capable of handling real-world complexity requires deliberate architectural choices that go beyond stringing together LLM API calls.
The agent loop
Every agent is built around a loop: observe, reason, act, repeat. The observation is what the agent currently knows — its goal, the current state of the world, the results of its previous actions. The reasoning step is an LLM call that decides what to do next. The action executes that decision. The updated observation incorporates the action's result, and the loop continues.
The simplest architectures run this loop directly with a single model. More sophisticated architectures separate the reasoning and acting: a planner model decides on a high-level strategy, an executor model performs individual steps, and a verifier model checks that each step succeeded before proceeding. This separation makes each component smaller, faster, and easier to test.
Planning and task decomposition
Agents that attempt long-horizon tasks without explicit planning tend to lose coherence. An agent asked to "implement the user authentication system" will make better decisions if it first produces a plan — a list of subtasks in order — than if it jumps directly to implementation. The plan becomes a structured context artifact: the agent can check its progress against the plan, recover from errors by resuming from a known state, and surface the plan to the user for review.
Effective planning patterns: hierarchical decomposition (break the goal into phases, phases into steps, steps into atomic actions); dependency ordering (identify which steps depend on which, and sequence them correctly); and checkpoint definition (identify points where the agent should verify its progress and potentially involve a human). A good plan also flags uncertainty — steps where the agent isn't sure what to do are better surfaced than silently guessed at.
Tool use design
Agents act through tools: function calls that read files, execute code, search databases, call APIs, or write to storage. Tool design is as important as model selection. A tool that does too much in one call is hard for the model to use precisely; a tool that does too little requires the model to chain many calls to accomplish simple things.
Good tool design principles: tools should have single, clear purposes with predictable behavior; they should return structured, parseable outputs rather than prose; they should fail explicitly with actionable error messages; and they should be idempotent where possible so that retries don't cause side effects. The model learns to use tools from their descriptions — invest in writing clear, precise tool descriptions that specify inputs, outputs, and failure modes.
Memory architecture
Agents need memory at three timescales: within-step (what happened in this reasoning cycle), within-session (what happened in this conversation), and cross-session (what happened before this conversation started). Each timescale requires different storage.
Within-step memory lives in the context window. Within-session memory is the conversation history, often compressed via rolling summarization as it grows. Cross-session memory requires external storage — planning documents, task logs, handoff summaries — that the agent reads at the start of each session. Design all three explicitly; agents that don't have explicit cross-session memory restart effectively from zero each time.
Error recovery and robustness
Agents fail. Tools return unexpected outputs, models make incorrect decisions, and the environment changes in ways the agent didn't anticipate. Robust agents handle failure gracefully: they retry transient errors, escalate to a human when stuck, and avoid cascading failures where one bad step corrupts everything downstream.
Defensive design patterns: validate tool outputs before acting on them; implement retry logic with exponential backoff for transient failures; set maximum iteration counts so agents can't loop indefinitely; checkpoint progress so recovery from failure doesn't require restarting from scratch; and log every action and observation so failures can be diagnosed.
Evaluation and testing
Agent evaluation is harder than model evaluation: the output is a sequence of actions, not a single response, and the correct sequence may vary. Effective approaches: trajectory evaluation (did the agent take the right steps in the right order?); outcome evaluation (did it achieve the goal, regardless of path?); and unit testing individual tools and reasoning steps in isolation before testing the full agent.
See Multi-Agent Systems & Context for patterns for coordinating multiple agents, or AI Agent Context Management for a deep dive on managing context within an agent loop.