What an AI Agent Actually Is
An AI agent is usually a system that combines:
<ul>
<li>a model</li>
<li>a goal</li>
<li>tool access</li>
<li>memory or state</li>
<li>logic for taking multi-step actions</li>
</ul>
The important distinction is not the word "agent." It is whether the system can plan, act, observe results, and continue toward a task with limited human input.

Which product is closest to a true agentic workflow?

Agents are most useful when the task has a clear goal, available tools, and verifiable intermediate steps.

Where Agents Fit and Where They Do Not
Agents work best when:
<ul>
<li>the task has structured steps</li>
<li>tools exist for taking action</li>
<li>success can be checked</li>
<li>the cost of mistakes is manageable</li>
</ul>
They are a poor fit when the task is vague, high-risk, or impossible to verify.

Which situation is the weakest fit for an autonomous agent?

Autonomous agents work best when objectives are vague and there is no reliable feedback on whether they succeeded.

Common Failure Modes
Agentic systems can fail by:
<ul>
<li>compounding small errors across steps</li>
<li>looping or calling the wrong tools</li>
<li>acting with the wrong permissions</li>
<li>creating hidden latency or cost</li>
<li>looking successful in demos but failing in edge cases</li>
</ul>
This is why agent design needs guardrails, not just ambition.

Which guardrail is most important for an agent that can take real actions?

More autonomy always improves user experience.

How PMs Should Evaluate Agents
PMs should look at:
<ul>
<li>task success rate</li>
<li>intervention rate</li>
<li>time saved</li>
<li>cost per completed job</li>
<li>error recovery quality</li>
<li>user trust</li>
</ul>
The safest launch path is usually narrow scope, strong instrumentation, and human review where the downside is meaningful.

Which launch strategy is strongest for a new agentic feature?

PMs should define stop conditions, rollback paths, and review thresholds before increasing autonomy.