Agents posted to a public wiki for two months before anyone noticed. Clients will read that before your next steering meeting. Have a real answer ready, not a policy PDF. The domains your agents can write to, and who signed it off. Governance that is not config is theatre.
Clients pick their most visible process for the first AI build. Wrong test. Pick the one that runs 400 times a week and bores everyone. Visible pilots get judged on vibes. Repetitive ones produce an eval set and a real number by week three.
You do not have an AI system in production until someone gets paged when it breaks. Most delivery stops at the demo and hands over a build with no owner, no alert and no fallback. Ship the on call runbook with the model. That is the line between pilot and production.
Before you quote an AI build, ask the client for 50 real examples of the task. Actual messy inputs, correct outputs. Half cannot produce them. That is not a data problem. It means nobody has decided what right looks like. No model fixes that.
Every AI proposal we see prices the build and nothing after it. Then the model version changes, the source documents change, and the thing quietly gets worse with nobody watching. The run contract is worth more than the build. Quote both or you are selling a decaying asset.
Cache reads dropped 75% yesterday. That changes scoping, not pricing. Retrieval loops too expensive to run per ticket are viable now. Go back through the proposals you cut down on cost this year. Two of them are buildable this quarter.
The highest leverage hire in AI delivery is not a prompt engineer. It is whoever owns the eval set. Prompts take a weekend. Knowing an agent broke before the client rings you is the entire business. We staff evals before we staff build.
GPT-6 Astra ships and every agency with an RPA line item just got repriced. The clicking was never the billable part. What survives is defining which calls the agent may make and who owns it when it is wrong. Sell the judgment boundary, not the script.
Billing AI delivery by the hour means every efficiency you ship cuts your own invoice. Clients can smell that incentive. Price the outcome and keep the speed you built. An agency that bills hours on AI work is capping itself on purpose.
NIST flagged it this week. Most agentic deployments run on static API keys and long lived tokens. So ask for the credential list before you quote on any inherited AI build. It is the fastest read on whether the last team shipped production or a demo.
The in-house AI build is not taking our work. It is taking the easy first year of it. Clients ship v1 alone now, then call when it meets real data, real permissions and real edge cases. Sell the hard 20%. Stop bidding on the easy 80%.
Meta nearly cut 60% of some teams on the strength of AI output, then pulled back. Output rose. Quality fell. Same trap in AI delivery. If you are not tracking rework rate, your throughput number is fiction.
Agency leverage is not headcount. It is the second time you deliver the same build.
Build one is custom and thin. Build five is a product with a services wrapper, and it funds everything else.
Sell build five. Stop selling build one.
Most AI transformation programs die in the pilot. Not because the model is wrong. Because nobody owned the workflow it was supposed to replace. Pick the workflow first. Then pick the model.
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