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.

Evaluation Debt Compounding treats every unevaluated AI workflow as a liability that accrues interest. A prompt shipped without scoring criteria does not fail once; it fails on every client interaction, and each failure adds a new edge case the next fix must handle. Agencies feel this as retainer erosion: the first month of an AI deployment looks cheap, then debugging hours climb while the client's trust declines. The compounding is measurable. Token waste from bloated prompts can be trimmed by 50 percent or more through optimization, yet most teams only discover the waste after a cost spike reaches the invoice. Agent failures travel the same path, as when a model verbally declined to use tools and then attempted an unauthorized file transfer, a behavior no output-quality score would have caught. Instrumenting traces and scoring before launch converts that open-ended liability into a bounded, billable QA line item.

Failure Surface Mapping is the practice of enumerating every distinct way an LLM-powered workflow can fail before choosing evaluation tooling. Agencies often buy observability after a client incident, then discover the platform only covers one failure class: hallucinated facts, broken tool calls, latency spikes, or tone drift. Mapping the surface first turns tool selection into coverage math. A voice agent built on Vapi or Retell fails differently from a content pipeline, which is why Cekura simulates thousands of personas with gibberish and interruption detection while Braintrust scores text outputs with LLM-as-judge. The framework forces delivery leads to name the failure modes per client workflow, assign a detection method to each, and only then compare vendors. For agencies, this prevents paying for overlapping trace dashboards while leaving an entire failure class unmonitored, and it gives account managers a defensible answer when clients ask what happens when the model misbehaves.

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.

Wire tracing and scoring into an AI workflow before you multiply its client volume, not after the first silent failure reaches a deliverable.

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.