Most AI agents are glorified chatbots. Here is how to build one that takes action, remembers context, uses tools, and improves over time.
Most so-called "AI agents" are chatbots with fancy prompts. They take a question, generate a response, and forget everything. A real agent takes action: it reads your calendar, drafts emails, updates your CRM, monitors your metrics, and learns from its mistakes. The difference is tools and memory.
Every functioning AI agent needs four things: (1) A brain — the LLM that reasons and decides. (2) Tools — APIs and integrations that let it take action in the real world. (3) Memory — persistent context that survives across sessions. (4) Goals — clear objectives with success criteria so it knows when it is done.
Claude Code provides the brain. MCP servers provide the tools (GitHub, Slack, databases, web scraping). CLAUDE.md and the 7-tier memory system provide persistence. Your task definition provides the goals. Wire them together and you have an agent that can operate autonomously — reading tickets, writing code, running tests, and creating pull requests.
Session memory captures what is happening now. Working memory (memory.md) holds the current project context. The knowledge base stores learned rules across sessions. Agent memory gives each specialist its own persistent state. The knowledge graph maps entity relationships. Daily notes provide an audit trail. Without this stack, your agent forgets everything between conversations.
Mistake 1: Starting with a complex multi-agent system — start with one agent doing one job well. Mistake 2: No memory system — your agent re-learns the same things every session. Mistake 3: Too many tools at once — start with 2-3 MCP servers and add more as needed. Mistake 4: No evaluation — measure your agent's success rate and iterate on the prompt, not just the tools.
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