Agentic AI — Anatomy of Autonomous Agents

Ask ChatGPT a question and you get an answer. If it stops there, it's a chatbot.

Agents are different. Say "fix this bug" and it reads code, analyzes the cause, creates a fix, runs tests, and if something's wrong, fixes again. Without human intervention.

The Core Agent Loop

Every agent system boils down to this loop:

1. Observe: Assess current state (read files, check error logs, search)
2. Think: LLM analyzes situation and decides next action
3. Act: Call tools (edit code, API calls, create files)
4. Evaluate: Check results → judge whether goal is met
5. If not met → back to step 1

Claude Code, Cursor, Devin — all coding agents follow this pattern.

Agent vs Workflow

A distinction Anthropic draws:

Workflow: LLM calls are orchestrated through predefined code paths. Fixed order.

Agent: LLM drives the process itself, autonomously deciding tool use and next steps.

The difference is "who holds control." Workflows have developers design the flow with LLM filling in; agents have the LLM decide the flow itself.

Implementation Essentials

No complex framework needed. A while loop + LLM API + tool definitions is enough for a basic agent.

while not done:
  response = llm.chat(messages, tools)
  if response.has_tool_call:
    result = execute_tool(response.tool_call)
    messages.append(result)
  else:
    done = True

Anthropic's official guide also emphasizes "start as simple as possible." Frameworks add complexity but don't always add value.