01
Start with a bounded operating decision
A credible agent initiative begins with a workflow, decision, or service outcome. It does not begin with a model demonstration. Define the actor, trigger, expected output, time sensitivity, and consequence of a wrong answer before discussing architecture.
The best early candidates usually involve repeatable knowledge work, accessible source material, a clear escalation path, and enough volume to justify integration and evaluation.
- What event starts the workflow?
- Which tools or records must the agent use?
- What can the agent decide, recommend, or prepare?
- Where must a person review or approve the result?
02
Confirm that the knowledge can be governed
Retrieval quality depends on source quality. Teams should know which documents are authoritative, who owns them, how permissions are enforced, and how outdated material is retired. A larger knowledge base does not compensate for weak ownership.
Access controls should follow the user and the source system. Copying every document into an unrestricted index creates a new security boundary that the organization must operate.
03
Define evaluation before production
An agent cannot be judged only by whether a few demonstrations look persuasive. Build a representative set of tasks, expected behaviors, unacceptable failures, and escalation rules. Measure answer quality, tool use, latency, cost, and human correction.
Evaluation should remain in the delivery pipeline so changes to prompts, tools, models, and retrieval logic can be compared before release.
- Representative task dataset
- Quality and refusal criteria
- Tool-use and permission checks
- Latency and cost budgets
- Human review and incident process
04
Treat adoption as part of the system
A technically sound agent can still fail if users do not know when to trust it, how to correct it, or where responsibility sits. Training, workflow design, usage guidance, and feedback collection belong in the implementation plan.
A readiness assessment should end with a clear decision: proceed, narrow the use case, repair the data foundation, or stop. Avoiding the wrong build is a valuable outcome.
Apply this to a live decision