Implementation BlueprintExecution layer

ClientCoded Agent Launch Sprint (5-7 days)

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. Time: 5-7 days.

By InnovaAI ResearchPublished

AI Evaluation Observability

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Blueprint

ClientCoded Agent Launch Sprint (5-7 days)

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.

Prerequisites
  • ClientCoded workspace registered and a Slack workspace connected for real-time quality alerts
  • Client's database schema description and agent endpoint supplied (both are required inputs for environment generation)
  • Named client-side owner for the agent who can approve the adversarial query suite and failure thresholds
  • Access to the client's integration stack so the correct pre-built environment is selected from the 40+ connectors (Salesforce, Jira, Stripe, and others)
  • Agreed definition of a passing score on answer correctness and conversation quality before the first test run
Execution Timeline
  • 1.Register the agency workspace and connect the client's Slack channel for drop alerts
  • 2.Walk the client through the 40+ pre-built environments and shortlist the ones matching their live integrations
  • 3.Collect the schema description and agent endpoint, the two inputs ClientCoded needs before it can generate anything
  • 1.Generate the synthetic test environment from the client's schema and confirm the seeded records match real workflows
  • 2.Review the seven adversarial query categories and flag any that miss the client's known failure modes
  • 3.Set the baseline scoring thresholds for answer correctness and conversation quality
  • 1.Run the 200-query adversarial suite against the client's agent endpoint
  • 2.Pull per-question transcripts and group failures by root cause rather than by query
  • 3.Log which failures are schema gaps versus genuine agent reasoning errors
  • 1.Send the failure list to the client's engineering owner with the exact transcript for each miss
  • 2.Re-run the suite after their first round of fixes to measure movement against the day 2 baseline
  • 3.Document any query the agent cannot pass regardless of fix, and mark it as an accepted limitation
  • 1.Switch on production monitoring and confirm conversations are being scored in real time
  • 2.Verify Slack alerts fire on a deliberately degraded test conversation before trusting them in production
  • 3.Hand the client a written escalation path for what happens when an alert fires at 2am
  • 1.Assemble the reliability scorecard: baseline score, post-fix score, open failures, accepted limitations
  • 2.Record a short walkthrough of the monitoring dashboard for the client's non-technical stakeholders
  • 3.Agree the monthly monitoring cadence and who owns the review call
  • 1.Deliver the scorecard and monitoring handover in a single session
  • 2.Propose the ongoing retainer covering scheduled monitoring and monthly re-tests
  • 3.Archive the test environment configuration so the next agent build starts from a known state
$3,500 fixed fee for the sprint, against $49/month for the Starter tier (1 test environment, 1 adversarial run per month, 10,000 messages scored) or $599/month for the Team tier (5 agents, 60 full tests per month, 5,000 production conversations monitored) when the client runs multiple agents.5-7 days
ROI Logic

The sprint is priced at $3,500 while the tool cost behind it is $49/month on Starter, so a single engagement covers years of the platform fee and the margin sits in the schema work, adversarial suite tuning, and failure triage that the client cannot do without an agency. Moving the client onto the Team tier at $599/month only makes sense once they run several agents, which is exactly the point where the monitoring retainer becomes recurring revenue rather than a one-off project.

Deliverables
  • ClientCoded reliability scorecard showing baseline and post-fix scores across the 200 adversarial queries
  • Per-question failure transcripts annotated with root cause and owner
  • Configured production monitoring with Slack alert routing and escalation path
  • Accepted limitations register listing queries the agent cannot pass and why
  • Test environment configuration archive for reuse on the client's next agent build
Definition of Done

The client's agent has a documented pass rate on the 200-query adversarial suite, production monitoring is live with a verified Slack alert, and the client has signed off on the open failure list.

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