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.
Build Production Agents at CodeLeap
AI agent development is the hottest skill in software engineering right now. Companies are paying premium salaries for developers who can build reliable, production-grade agent systems.
CodeLeap's Developer Track includes a dedicated agent-building module where you'll build a multi-tool AI agent from scratch, implement memory systems, and deploy it to production. The capstone project: a multi-agent system that collaborates to complete complex tasks.