Reliable AI Agents Require More Than Tool Calling

I built an autonomous AI agent with 9 tools. Here’s how it actually works.

For the last 3 months, I’ve been building MindStash(https://www.mindstashhq.space/) — an AI-powered personal knowledge system. The core of it is an agent built on Claude Haiku 4.5 that can search, create, update, delete items, generate briefings, and manage notifications.

The interesting part isn’t the tools themselves. It’s the orchestration:
→ The agent decides which tools to call based on user intent
→ When multiple tools are independent, they execute in parallel
→ Destructive actions (delete, bulk operations) trigger a human-in-the-loop confirmation
→ The agent has long-term memory — it extracts preferences from conversations and injects them into future system prompts
→ Everything streams via SSE with structured events (text_delta, tool_start, tool_result, done)

The hardest part? Error handling. When a tool fails mid-conversation, the agent needs to gracefully recover and explain what happened without breaking the chat flow.

Built with: FastAPI + Claude API + PostgreSQL + SSE streaming

If you’re adding AI agents to your product, the tool-calling loop is just the beginning. The real work is in making it feel reliable. #AIEngineering #BuildInPublic #SaaS #Claude #FastAPI