Agent Chain Pre-Flight (Onboarding)

Agent Chain Pre-Flight (Onboarding)

1. 01

   Map the client's current manual handoffs before proposing any agent chain

   List each step where work moves between people or tools, note who owns it, and record the average elapsed time. A three-stage chain (extract, draft, review) that mirrors an existing handoff is easier to defend in a retainer conversation than a redesigned process.

2. 02

   Assign one named owner per agent and one owner for the chain as a whole

   Platforms such as AgentX ship role definitions and evaluation pipelines, but accountability still sits with a person. Without a chain owner, failures get routed to whoever is closest to the client rather than to the operator who can fix the logic.

3. 03

   Define the pass and fail condition for every agent boundary

   Write the exact output shape the next agent expects, plus the condition that stops the chain. A compliance check that returns an ambiguous verdict should halt, not pass through to publishing.

4. 04

   Set a fallback path for each stage before go-live

   Decide in advance whether a failed stage retries, routes to a human queue, or returns a partial deliverable. Document the retry ceiling so a looping agent does not burn API budget overnight.

5. 05

   Confirm data residency and access scope against the client's contract

   Self-hosted options such as Pacific Slate keep the knowledge base on the operator's own hardware, while enterprise platforms like StackAI offer VPC and on-premise deployment. Match the deployment model to what the client's legal review will actually approve.

6. 06

   Instrument logging and cost tracking at the chain level, not just per agent

   Capture input, output, latency, and token spend for every hop. Frontier model pricing moved sharply in September 2026, with Claude Opus 5.5 landing 40% below Opus 5, so per-chain cost baselines need a review cadence rather than a one-time figure.

7. 07

   Run the chain against a frozen test set and record the baseline score

   Freeze 20 to 30 real client examples, run the full chain, and store the output alongside the score. This baseline is what you compare against after any model or prompt change.