What Are AI Agents?
"An agent isn't smarter than an LLM — it's an LLM that can take actions, observe results, and keep going until a job is done."
A traditional LLM call is stateless: you send a prompt, you get a response. Done. An AI agent is different: it can take multiple steps, use tools to interact with external systems, observe what happened, and decide what to do next — all autonomously. Agents are LLMs given a loop, memory, and hands. They can browse the web, write and execute code, send emails, query databases, and chain these actions together.
The Agent Loop
"Every agent runs the same basic loop, no matter how complex the task: perceive, reason, act, observe, repeat."
The agent loop is the core operating pattern for all AI agents. Perceive: the agent receives input (user request, tool result, environment state). Reason: the LLM thinks through what to do next (often with chain-of-thought). Act: the agent executes an action — calling a tool, writing output, or terminating. Observe: the result is fed back into context. This loop continues until the task is complete or a stopping condition is met.
Tool Use
"Tools are how AI gets hands — without them, an agent can only think. With them, it can act."
Tool use (also called function calling) lets agents interact with the outside world. A tool has a name, a description (so the LLM knows when to use it), and defined input/output schemas. When the LLM decides to use a tool, it generates a structured tool call; the runtime executes it and returns the result. Common tools: web_search, run_python, read_file, send_email, query_database, call_api.
Multi-step Orchestration
"Complex AI tasks aren't one big prompt — they're pipelines where each step feeds the next."
Orchestration is about coordinating multiple LLM calls, tool uses, and decision points into a coherent workflow. Simple orchestration: prompt A → result → prompt B. Advanced: parallel steps, conditional branching, retry logic, and human checkpoints. Frameworks like LangChain, LlamaIndex, and Anthropic's agent SDK help manage this complexity. The key insight: break big problems into small, verifiable steps.
Agents vs Simple Prompts
"Not everything needs an agent. Reach for a simple prompt first — add an agent only when you need memory, tools, or multiple steps."
Agents are powerful but expensive, slow, and harder to debug. Use a simple prompt when: the task is one step, doesn't need external data, and can be done in a single LLM call. Use an agent when: the task requires multiple steps, needs real-time data or tool use, benefits from iteration and self-correction, or has conditional branches. The biggest mistake in AI engineering is over-agentification — using agents where a well-crafted prompt would work better.
Building Your First Flow
"The best way to learn orchestration is to build one small flow and make it production-ready before adding complexity."
Start with a simple three-step flow: Trigger (something happens) → Process (Claude does something intelligent) → Deliver (result goes somewhere useful). Example: new Slack message → Claude summarizes the thread and identifies action items → sends a structured digest to a Notion database. Use Zapier or Make if you don't code. Use Python + Anthropic SDK if you do. Measure how well it works before adding steps.
Multi-agent Pipelines
"One generalist agent trying to do everything is worse than three specialized agents that each do one thing well."
Multi-agent pipelines split complex tasks across specialized agents. A research pipeline might have: Agent 1 (searches the web, collects sources), Agent 2 (reads and synthesizes sources), Agent 3 (writes the final output), Agent 4 (reviews for errors and citations). Each agent is smaller, faster, cheaper, and easier to evaluate than one monolithic agent. Communication between agents can be direct or through shared memory/databases.
Pitfalls & Fixes
"Every production agent breaks in a way you didn't anticipate. The fix is always: more structure, better tools, tighter evals."
The five most common agent failure modes: (1) Infinite loops — the agent keeps calling tools without making progress. Fix: add step limits and goal-checking. (2) Tool call failures — the API returns an error. Fix: add retry logic and graceful fallbacks. (3) Context overflow — too much history fills the context window. Fix: summarize history periodically. (4) Hallucinated tool calls — the agent invents tools that don't exist. Fix: strict tool schema enforcement. (5) Cost runaway — too many API calls. Fix: budget limits per run.
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