How We Built a Production AI Agent in 3 Weeks

Building AI agents that actually work in production is harder than most tutorials suggest. In this post we walk through the exact process we used to ship a multi-model autonomous agent for a US SaaS client — in 3 weeks flat.

The Problem

Our client needed an AI system that could autonomously research leads, draft personalized outreach emails, and log results to their CRM — all without human intervention.

Our Stack

• Orchestration: LangChain + custom agent loop
• Models: GPT-4o for reasoning, GPT-4o-mini for fast tasks
• Memory: Pinecone vector database for long-term context
• Backend: Python + FastAPI
• Deployment: AWS Lambda + API Gateway

Week 1: Architecture & Prompt Engineering

The first week was entirely planning and prompt work. We designed a ReAct agent loop with 4 tools: web search, CRM read/write, email draft, and a quality-scoring function.

Getting the prompts right took 3 full days of iteration. The key insight: agents fail not because of the model, but because of unclear tool descriptions and missing error recovery paths.

Week 2: Building & Testing

We built each tool in isolation first, tested edge cases, then integrated. The hardest part was rate limiting — the agent would sometimes fire 40+ API calls in 10 seconds.

Solution: a token bucket rate limiter with exponential backoff.

Week 3: Hardening & Launch

Production readiness checklist:

• Structured logging with Datadog
• Human-in-the-loop approval for emails over $10K deals
• Fallback to GPT-4o-mini when GPT-4o is slow
• Cost monitoring alerts at $50/day

Result: The agent now processes 200+ leads per day with 73% open rates on AI-drafted emails. The client's SDR team focuses only on warm leads.

Key Takeaways

1. Spend more time on prompts than code

2. Always have a human-in-the-loop for high-stakes actions

3. Rate limiting is not optional

4. Log everything — debugging agents is hard

Want us to build something similar for your team? Start with a discovery call →