What Are AI Agents?
AI agents are the next evolution beyond chatbots. While a chatbot responds to a single prompt, an agent:
- 1Receives a goal (not just a prompt)
- 2Plans how to achieve it (breaks it into steps)
- 3Executes each step using tools (APIs, databases, file systems)
- 4Observes the results
- 5Adapts its plan based on what happened
- 6Iterates until the goal is complete
Examples: Claude Code is an agent (it plans, writes code, runs tests, fixes errors). Devin is an agent. Any system that autonomously completes multi-step tasks is an agent.
Agent Architecture: The Core Loop
Every AI agent follows the same fundamental loop:
Observe → Think → Act → Observe → ...
- 1Observe: Read the current state (user input, tool outputs, environment data)
- 2Think: Use an LLM to reason about what to do next
- 3Act: Call a tool (search the web, query a database, write a file, run code)
- 4Observe: Read the tool's output and update understanding
The agent continues this loop until it determines the goal is achieved or it encounters an unrecoverable error.
Key design decision: How much autonomy to give the agent. More autonomy = faster execution but higher risk. Less autonomy = safer but requires more human oversight.
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Building Blocks: Tools, Memory, and Planning
Tools are functions the agent can call. Examples: - `search_web(query)` — search the internet - `read_file(path)` — read a file from the filesystem - `run_code(code)` — execute Python/JavaScript - `query_database(sql)` — query a database - `send_email(to, subject, body)` — send an email
Memory lets agents remember across interactions: - Short-term: The current conversation (context window) - Long-term: A vector database storing past interactions and knowledge - Working memory: A scratchpad for intermediate results
Planning strategies: - ReAct: Reason, then Act. Simple and effective. - Plan-and-Execute: Create a full plan upfront, then execute step-by-step. - Tree of Thought: Explore multiple approaches in parallel, pick the best.
Implementation: Your First Agent
Here's the simplest possible agent architecture:
- 1Define your tools as functions with clear descriptions
- 2Create a system prompt that describes the agent's role and available tools
- 3Send the user's goal to the LLM along with tool descriptions
- 4Parse the LLM's response — if it calls a tool, execute it and loop back
- 5If the LLM responds with a final answer, return it to the user
For production agents, use a framework: - LangChain — Most popular, extensive tool ecosystem - CrewAI — Best for multi-agent systems - Anthropic Claude Agent SDK — Official SDK for building Claude-powered agents - Custom — Build from scratch for maximum control
Start simple. A single agent with 3-5 tools is more useful than a complex multi-agent system that's hard to debug.
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