A workflow follows a fixed path, while an AI agent makes dynamic decisions based on real-time inputs.
✔️ Workflows – Predefined automation steps (e.g., "if X happens, do Y").
✔️ AI Agents – Decide next steps dynamically based on reasoning (e.g., "Given this new data, what should I do?").
✔️ Test: If your system always follows the same steps, it’s not an agent—it’s a workflow.
🔹 Pro Tip: Use CrewAI or GooseAI Framework to add reasoning & dynamic decision-making.
Most successful AI agents start small and evolve through iteration.
✔️ MVP Approach: Begin with one API call + structured output.
✔️ Avoid over-engineering: Too many APIs & tools = increased failure points.
✔️ Optimize incrementally: Use feedback loops to improve agent behavior over time.
🔹 Pro Tip: Start with a basic Retrieval-Augmented Generation (RAG) system before adding complex features like memory & multi-agent coordination.
Without structured feedback, AI agents will never improve in production.
✔️ Unit tests – Validate output accuracy for coding agents.
✔️ Recursive search – Ensure research agents refine answers iteratively.
✔️ Human-in-the-loop (HITL) – Critical for high-stakes applications (finance, legal, healthcare).