AI Agent
Software that pursues a goal by taking its own steps — deciding, acting, and reacting — instead of answering once and stopping.
When not to use it
- When a single well-crafted prompt does the job. Agents add planning loops, tool calls, retries, and failure modes. If one call answers the question, one call is the architecture.
- For anything irreversible without a human in the loop. Sending emails, moving money, deleting records — an agent that's right 95% of the time is a system that's wrong every twentieth action, unsupervised.
- When you can't afford non-determinism. The same input can take a different path each run. If your users or auditors need reproducibility, an agent is the wrong shape.
Reach for something else instead
- A workflow — fixed steps, model calls at specific points. Boring, debuggable, and correct for the majority of "agentic" projects.
- A single prompt with tools when you need one lookup, not a plan.
- Human-in-the-loop review for anything consequential. Slower on paper, faster once you count the incidents.
Sources & further reading
- Yao et al. (2022), ReAct: Synergizing Reasoning and Acting in Language Models — the interleaved reason-then-act loop most agent frameworks are built on.
- Schick et al. (2023), Toolformer — models learning when to call a tool, rather than being told.
- Shinn et al. (2023), Reflexion — self-critique loops, and an honest look at where they stop helping.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Giving an agent more autonomy than the task requires, then being surprised by the blast radius.
- No budget or step limit. Loops that can't terminate are the classic agent failure — and the bill arrives regardless.
- Treating tool errors as edge cases. In production, tools fail constantly; how the agent handles a failed call is the product.