Negative expected value is allowed

Not every ticket deserves an agent. That sentence sounds obvious until you watch a team agent-wash a one-line string fix, a confidential design review, or a research spike with no acceptance criteria — then spend longer reviewing the diff than typing the change would have taken.

Coding agents are leverage under the right distribution of tasks. Under the wrong one they are a tax: prompting, waiting, auditing, and undoing confident mistakes. The METR randomized study on experienced open-source developers (early 2025 tooling) found participants took about 19% longer with AI on familiar repos — while still believing they were faster. Perception is not a ship metric. Expected value is.

Assistant vs agency

It helps to separate two products that share a chat box:

An assistant speeds mechanical work when you can verify line-by-line. Agency needs a clear goal, a navigable repo, and a verifier — or it externalizes judgment while leaving you accountable. I wrote about assistant-style Compose UI as a UI/IME pattern: streaming bubbles, keyboard insets, stable list keys — not as a claim that Mail shipped a product assistant. This one is about when you should refuse agency on a ticket. Different layer; same word. Do not confuse a helpful panel with an unsupervised worker.

flowchart LR
  ticket[Ticket] --> ev{Expected value?}
  ev -->|high| agent[Agent with checks]
  ev -->|low| hand[Do it yourself]
  ev -->|unclear| spike[Time-boxed spike only]
  agent --> verify[Tests / review]
  spike --> decide[Promote or stop]

Figure 1. Default is not “always agent.” Route by expected value, then verify.

Negative-EV ticket shapes

Unclear product intent. “Make it feel premium” has no falsifiable done. An agent will invent UI and you will argue with a mirror. Write acceptance first (planning with teeth) or keep the work human.

One-line / known-locus fixes. Typo in a string, wrong constant, obvious NPE with a stack frame pointing at one method. The prompting overhead exceeds the edit. Use the agent as autocomplete if you want — do not open a multi-tool investigation.

Confidential or political review. Security-sensitive diffs, personnel issues, unreleased metrics, legal holds. Pasting context into a cloud agent can be a policy violation; even when allowed, you need a human who can swear to what shipped. Agency here is not speed — it is risk transfer theater.

Novel research with no oracle. Greenfield spikes where you do not yet know what “correct” means. Agents optimize for plausible completeness. You need exploration notes, not a PR that pretends the design exists.

Tasks where you are the expert and the suite is thin. On mature code you know cold, review cost dominates. METR’s setting — experienced developers on their own large repos — is exactly where “AI will 10× me” is most likely to lie. Thin golden evals help configs (ship bar ≠ demo); they do not erase the review tax on every ticket.

Rule of thumb - if explaining the task to the agent takes longer than doing it, you already have your answer.

Don’t agent-wash everything

Agent-washing is the organizational failure mode: every Jira ticket gets an agent run because leadership bought seats, or because “AI-first” is a performance narrative. Symptoms:

• PRs with huge unrelated refactors the human did not ask for
• Review queues full of bot comments nobody owns (public wrongness)
• Engineers who cannot find the one-shot test command because the map was never written (navigable monorepos)

Seat utilization is not productivity. Track outcomes you already care about: cycle time to green CI, revert rate, review latency. If those worsen while agent usage climbs, you are paying for theater.

When agency is positive EV

Use agents hard when the work is bounded, checkable, and boring at scale: mechanical API migrations across many call sites, test scaffolding with a compiler as verifier, summarizing a failing CI log into a hypothesis you then validate, drafting a first-pass patch inside a module whose commands are documented. Pair with tests and path bans. Keep humans on intent, architecture, and anything that must be true in production — not on typing every rename.

Wrap-up

Refuse agency on unclear, tiny, confidential, or oracle-free tickets. Keep assistants for mechanical help you can audit in seconds. Measure yourself against wall-clock and reverts, not against how busy the sidebar looks. The mature move is not “AI everywhere” — it is knowing when the expected value is negative and closing the chat.

References

• METR: early-2025 AI and experienced open-source developer productivity — ~19% slowdown vs perceived speedup on familiar repos
• Ars Technica summary of the METR study — review/prompt/wait overhead eating coding-time savings
• 5 things to avoid when working with AI coding tools — scope, review burden, and context bloat