An "agent" sounds exotic, but the core is a plain loop: the model observes state, decides on an action, acts through a tool (search, code execution, an API), reads the result, and repeats until the task is done. The model is the easy part now. The reliability is where the work is.
The loop, and where it breaks
Most agent failures are not the model being unintelligent — they are the scaffolding around it:
- Error recovery. A tool returns an error or garbage. Weak agents plow ahead; good ones notice, diagnose, and retry differently.
- Context management. Long tasks overflow the context window. What the agent chooses to remember, summarize, or drop decides whether it stays coherent.
- Stopping. Knowing when the task is complete — or hopeless — is a skill. Agents that can't stop loop forever or declare victory too early.
Tools are the product surface
An agent is only as capable as the tools you give it and how clearly they're described. A crisp tool definition — good name, tight schema, honest description of when to use it — does more for reliability than a smarter model. Ambiguous tools produce confused agents.
Give an agent ten overlapping tools with vague descriptions and it will pick wrong. Give it three sharp ones and it will look brilliant.
Evaluation is the bottleneck
You cannot improve what you cannot measure, and agents are hard to measure: success is multi-step and often subjective. Teams that win invest in task suites — reproducible scenarios with clear pass/fail — before they invest in prompts. The evaluation harness is the real moat.
Where it's heading
The near-term frontier is narrow, well-scoped agents that do one job dependably — coding, research, data cleanup — rather than a single agent that claims to do everything. Depth beats breadth, and trust is earned one reliable loop at a time.
