ClientCoded

ClientCoded prices validation at $49/month for 10,000 scored production messages and $599/month for 5 agents with 60 full tests, so an agency can bundle pre-launch proof and ongoing monitoring into one recurring fee instead of a one-off build. The 200 adversarial queries and computed ground truth give delivery teams a defensible accuracy number to show clients, which is what converts a project into a monitored retainer.

ClientCoded generates its synthetic test environment from a described database schema, so the quality of that schema description sets the ceiling on everything downstream: the 200 adversarial queries, the computed ground truth, and the accuracy score you eventually show a client. Run the gate before you quote a retainer. If the client's schema is undocumented or drifting, budget discovery hours first, because a vague description produces adversarial queries that miss the agent's real failure modes and a score that means nothing. A practical sequence for agencies: confirm schema access, generate one test environment from the 35 available, run the monthly adversarial pass, then only after a clean baseline attach the $49 Starter monitoring tier so production conversations get scored in real time with Slack alerts. Agencies that skip the gate sell validation they cannot defend when the client asks what the score actually measured.

Eval debt is the accumulated gap between what an AI agent does in production and what anyone on the agency team can actually prove it does. Like technical debt, it accrues quietly and charges interest: every untraced failure mode, every scoring rubric that lives in a Slack thread, every client demo that worked once and was never re-run. The interest payment arrives as a retainer conversation. Agencies that instrument early convert that debt into a premium line item, because "production-ready" is a claim only evidence can support. The cost curve is moving in their favor: OpenAI cut GPT-6 Sol and Luna API prices 50% versus GPT-5.6, and prompt caching now discounts up to 90% on reused prefixes, so high-volume agent pipelines are cheaper to run and cheaper to trace. Meanwhile Forrester's 2027 predictions flag compute and infrastructure constraints that will push API-dependent tool costs upward, compressing margins on AI-inclusive retainers. Tracing spend is the hedge.

The Silent Failure Surface is the set of AI behaviors that pass every automated check yet still damage the client relationship: a voice agent that interrupts callers, a support bot that loops a user through three retries, a research agent that returns confident but stale answers. Standard evals score outputs against expected answers, so they miss friction that only appears in live sessions. Agencies that map this surface before launch can price a monitoring retainer against it; agencies that skip it discover failures when the client forwards a complaint. Cekura simulates thousands of personas to expose interruption and gibberish patterns before go-live, while Agnost AI ingests real conversations and flags repeated retries and broken workflows as actionable intents. Both approaches treat production traffic as the primary test set, not a post-launch afterthought. The surface shrinks only when someone owns the loop between detection and a shipped fix.

Adopt ClientCoded only when the client's schema is documented and the engagement includes a monitoring retainer, otherwise sell the validation run as a one-off and walk away.

Instrument the AI workflow with tracing and scoring before you defend its output quality to a client.

IF your agency is deploying a client's first AI data agent and needs pre-launch proof of reliability, THEN the $49/month Starter tier gives you 1 test environment from 35 options, 1 adversarial run of 200 queries per month, and 10,000 messages scored in real time, which is enough to validate a single agent before go-live. IF you are running multiple client agents or need daily monitoring, THEN the $599/month Team tier (5 agents, 60 full tests per month, 5,000 production conversations monitored, 30 change-detection triggers) becomes the real operating cost, and you should only commit once a client retainer covers that line item.

A fixed-scope sprint that validates a client's AI data agent in a ClientCoded synthetic environment before production, then hands over live monitoring with Slack alerts and a reliability scorecard the client can defend to their own stakeholders.