I Replaced My Entire Research Workflow With AI Agents. Here's What Actually Worked

Short summary

Production AI agents work best as narrow, purpose-built systems with clear objectives, not general reasoning engines. Success depends on thoughtful tool design, failure handling, and observability—plus architectural patterns like plan-then-execute and separated retrieval. The real competitive advantage isn't chasing new models but building trustworthy systems other engineers can understand and maintain.

• •Real agents have objectives and handle failure; most are narrow, purpose-built systems, not general reasoning engines
• •Successful teams obsess over tool design, failure handling, and observability—not model selection or framework choice
• •Patterns (plan-then-execute, separated retrieval, explicit handoffs) matter more than frameworks or latest releases

Generated with AI, which can make mistakes.