AI Agents

AI agents are systems that act autonomously or semi-autonomously to accomplish goals. Unlike single-turn interactions with a language model, agents combine planning, tool use, memory, and iterative decision-making to complete multi-step tasks. They observe inputs, decide on actions, execute tools or queries, and repeat this loop until they reach a goal or hand control back to a human.

This guide explains the core concepts, common architectures, practical use cases, and design patterns for building reliable agents.

Core concepts

• Planner: the component responsible for high-level decisions (what to do next). The planner may be rule-based, model-driven, or a hybrid.
• Executor / tools: discrete capabilities the agent can invoke (search, web browsing, calculator, database queries, custom APIs).
• Memory: state the agent uses to track context, previous steps, or user preferences. Memory can be ephemeral (short-term buffers) or durable (embedding-backed long-term storage).
• Observability & logging: recording actions and outcomes so behavior is auditable and debugable.

Architectures and patterns

1. Loop-based agents (Perceive → Plan → Act → Observe)

   • Simple and effective for narrow tasks. A planner issues the next action, the executor runs it, and the agent ingests the result for the next cycle.

2. Planner-executor split

   • Separate modules: a planner (often an LLM) creates a plan (list of steps), and a specialized executor runs steps safely. This split lets you validate steps before execution.

3. Tools + function-calling

   • Modern LLMs support function-calling interfaces that let the model request structured actions. Wrapping tools with strict schemas reduces ambiguity and improves safety.

4. Hierarchical agents

   • For complex tasks, agents may decompose goals into subgoals and spawn sub-agents with narrower responsibilities.

Practical use cases

• Research assistants: iterate on search, summarize sources, and produce a short briefing.
• Automation: triage emails, generate follow-ups, manage scheduling workflows.
• Content production pipelines: draft → revise → fact-check → publish.
• Data extraction: query documents, extract structured data, and populate downstream systems.

Design considerations and best practices

1. Start small and scoped

   • Build agents for narrow, well-defined tasks before expanding. Scope reduction reduces errors and simplifies testing.

2. Limit tool surface area

   • Only expose the minimal set of tools needed. Each tool increases the attack surface and potential for misuse.

3. Add human-in-the-loop gates for important actions

   • For operations with real-world impact (transfers, actions affecting users), require human approval.

4. Log everything

   • Record inputs, decisions, tool outputs, timestamps, and who authorized actions. Logs are essential for debugging and trust.

5. Simulate and test

   • Create adversarial tests and unexpected inputs to evaluate how agents handle edge cases. Use synthetic negative tests to hunt for hallucinations or unsafe behavior.

6. Rate-limit and sandbox externally-called tools

   • Protect external APIs and systems with throttles and validation to avoid cascading failures.

Safety and alignment

Agents magnify both capability and risk. Key mitigations:

• Tool whitelists and parameter validation
• Output gating: run outputs through a classifier or a human reviewer before acting
• Conservative defaults and clear undo pathways

Example: a small research agent

1. Input: “Summarize recent defenses against model hallucination on finance data.”
2. Planner: produce a plan: (a) search web for papers, (b) extract abstracts, (c) summarize key techniques, (d) return citations.
3. Executor: call search API, fetch top results, parse abstracts, run summarization tool, and compile.
4. Output: a short summary with citations and a confidence level.

Getting started (practical steps)

1. Choose a narrow domain and assemble one or two reliable tools (search, summarizer, database).
2. Build a planner that outputs structured actions (simple JSON). 3. Implement an executor that validates actions and calls tools.
3. Add logging and a manual approval step.
4. Run tests with edge cases and refine.

Conclusion

AI agents are a practical next step beyond single-turn LLMs, enabling automation, orchestration, and multi-step problem solving. Success comes from careful scoping, tool design, observability, and conservative safety controls. Start with a small, auditable agent and iterate toward more capable workflows.