AI agent

• Tasks require multiple steps or decisions (e.g., research → draft → send).
• You need autonomy within guardrails, not just single-turn completions.
• Human operators are overloaded and consistency is poor.

Agents expand surface area and risk. PMs define scope, stopping rules, and observability. The payoff is faster delivery and fewer manual steps; the risk is runaway actions, cost spikes, or policy breaches. KPIs include task success rate, cost per task, and time-to-complete.

Start narrow: one clear goal, few tools, explicit stop conditions. Add step-level logging and replay. In 2026, pair agents with safety rails, per-tool budgets, and a lightweight overseer that can abort or request confirmation on high-risk steps. Ship with evals that mirror the full multi-step journey, not isolated steps.

A growth agent researches competitor pricing, drafts a comparison email, and schedules A/B tests. With tool budgets and approvals for send actions, it achieves 72% task success while keeping cost under $0.18 and p95 completion time under 90 s.

• Launching wide-scope agents without stop rules, causing loops and bills.
• Missing observability, so you cannot debug failures or user complaints.
• Letting agents call external APIs without permissioning or rate limits.