AI Agents: What Beginners Consistently Miss
Agents fail less on model quality than on state, tools, and error handling. A practical look at what to get right first.
Agents fail less on model quality than on state, tools, and error handling. A practical look at what to get right first.
It Is Rarely The Model
Most agent demos break in production for unglamorous reasons: no retry strategy, unclear state boundaries, and no observability into what the agent actually did. Swapping in a stronger model fixes none of these.
- Explicit state — know what the agent remembers and why
- Typed tools with validated inputs and useful error messages
- Retries with backoff, and a hard stop condition
- Logging good enough to reconstruct any run
Key Takeaway:
Design the failure paths before the happy path. Agents spend far more time in edge cases than demos suggest.
Start Smaller Than You Think
A single-step tool call that works reliably is worth more than a five-step plan that works occasionally. Add steps only once the previous one is boring.
“The hard part of agents was never the reasoning. It is everything around it.”
Written by
GFF AI Academy Team
Academy Editorial
