Building an AI Agent That Actually Works in Production
Most AI agent demos are impressive. Most AI agents in production are disappointing. I’ve been running Tree AI — a personal AI assistant — every day for over a year. Here’s what actually matters when you move from demo to production.
What Tree AI does
Tree AI is a multi-agent system that runs 24/7 on my server. It handles:
- Voice input via Whisper STT → AI response → voice output (TTS)
- Smart home control (lights, temperature, devices)
- Calendar and email management
- Market analysis and Telegram channel publishing
- Adaptive personas for different contexts
It’s not a side project or a toy. It’s the thing I use to manage my day.
What breaks AI agents in production
1. Context window management
The biggest killer. Long conversations accumulate context until you hit the limit or responses degrade.
My solution: structured compaction. Every N turns, I summarize the conversation into a structured object and start fresh with that summary as context. Not perfect, but it works.
2. Tool failure handling
Tools fail. APIs go down. Rate limits hit. An agent that crashes on first tool failure is useless.
The fix is simple but non-obvious: don’t let the agent decide what to do when a tool fails. Give it a strict protocol. “If tool X fails, try Y, then Z, then tell the user explicitly.” Hard-coded fallback chains beat smart failure handling every time.
3. The “loop of doom”
An agent gets confused, tries the same action repeatedly, fails, tries again. Without a circuit breaker, it runs forever.
I limit every agent run to a maximum number of tool calls. If it hits the limit, it stops and reports what it tried. This has saved me from infinite loops countless times.
4. Persona drift
Over long conversations, the agent slowly drifts from its intended behavior. Small language patterns shift, then tone shifts, then decision-making shifts.
Solution: a system prompt that’s re-injected periodically, not just at the start.
The MCP protocol changed everything
The Model Context Protocol (MCP) from Anthropic is the best thing that happened to agent development in 2024. Instead of writing custom tool integrations, you write MCP servers once and connect any compatible client.
My home automation, calendar, and file system all expose MCP servers. The agent connects and discovers what’s available. Adding a new capability is: write the MCP server, restart the agent.
What I’d tell someone starting now
Use a real language model. The gap between frontier models and small open models is enormous for real-world tasks. Budget for API costs.
Build observability first. Log every input, output, tool call, and error. You cannot debug what you cannot see.
Start with one use case. My first working version only did one thing well. I resisted the urge to add features until that one thing was solid.
It will break in ways you didn’t expect. Plan for failures you can’t imagine. Defensive programming matters more in agents than anywhere else.
The agent that works in production is not the one with the most capabilities. It’s the one that fails gracefully, stays within its scope, and recovers cleanly.