AI agents: architecture patterns, tools, and orchestration

AI agents are systems that use LLMs to reason about goals, plan actions, and execute tasks autonomously. Agents go beyond simple question-answering to take real actions in software systems. Building reliable agents requires careful design of the agent loop, tool use, and safety guardrails.

The agent loop is the core architecture. The LLM receives a goal and a list of available tools. It reasons about what step to take next, generates a tool call, and observes the result. This loop continues until the goal is achieved. The agent loop design determines how capable and reliable the agent is.

Tool definition is critical for agent performance. Each tool needs a clear name, description, and parameter schema. The LLM uses tool descriptions to decide which tool to use and how to call it. Well-defined tools produce reliable agents. Ambiguous tool descriptions cause errors.

Provide the agent with examples of successful task completion. Few-shot examples in the system prompt teach the agent the expected patterns. Examples of error recovery are especially important. A well-crafted example teaches the agent how to handle both success and failure.

Safety guardrails are essential for production agents. Limit the agent's tool access based on the principle of least privilege. Require human approval for destructive actions. Set maximum step limits to prevent infinite loops. Log every action for audit and debugging. Safety should be built into the agent architecture from day one.

Agents benefit from structured reasoning. Chain-of-thought prompting helps agents reason step by step. Reflection loops where the agent evaluates its own output improve quality. Structured reasoning patterns reduce errors compared to free-form reasoning.

Evaluate agents thoroughly before deployment. Test with a diverse set of goals, including edge cases and adversarial inputs. Measure task completion rate, steps required, error rate, and safety violations. Agent evaluation is more complex than model evaluation because of the interactive nature of agent tasks.

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Rizwan Saleem | https://rizwansaleem.co