Practical FDO guide
AI workflow automation: build a useful agentic workflow
AI workflow automation uses models for tasks such as interpreting information, extracting fields, or drafting within a business process. An agentic workflow can also choose steps or tools within defined boundaries. Use deterministic automation for predictable rules and calculations, and retain explicit ownership, review, and exception handling around AI decisions.
By AI Agentship · Published · Updated
When should you use rules, AI, or an agent?
Use rules when the required behavior is known: validate a date, sum accepted figures, route a form by a fixed category, or check that a required field exists. Use a model when the task involves language or ambiguous information that fixed rules cannot handle well. Neither choice removes the need for testing.
An agent adds flexibility when the next permitted step depends on what it finds. That flexibility introduces more operating cases to inspect. A fixed sequence with one model call may be sufficient; multiple agents are not a prerequisite for a useful workflow.
Map the workflow before connecting tools
For each step record: trigger, input, source owner, transformation, output, permitted action, reviewer, and failure route. Keep human decisions visible alongside automated steps. If nobody can explain who accepts the output, the workflow is not ready to operate.
Example design: an onboarding form passes required-field checks; an agent drafts a setup brief from approved client information; an operator verifies missing details; and a person approves the handoff. The draft does not authorize the agent to create accounts, send messages, or change customer records.
Give tools narrow responsibilities
Connect only the sources and actions needed by the workflow through supported APIs or MCP interfaces. Apply access controls in the connected systems and runtime. Written instructions in a task file are not a substitute for permission enforcement.
Define timeouts, retry limits, duplicate handling, and a human fallback. If a tool is unavailable, preserve the task state and show what remains incomplete. Avoid hidden retries that create repeated writes or turn a low-cost task into an expensive loop.
Test accepted work, not just successful tool calls
Test a normal case, incomplete input, conflicting evidence, access denial, and a rejected draft. Inspect the final artifact and action record. A successful API response only confirms a technical step; it does not establish that the team received a useful result.
An FDO uses Time-to-Value (TTV) to connect the implementation to its first verified business outcome. Track total delivery cost and human review effort as well. Retest after changing the model, knowledge, tools, or instructions, and expand only when repeated work meets the standard.
Implementation checklist
- Choose rules for predictable behavior.
- Define the workflow record and acceptance owner.
- Give agents only the tools the task needs.
- Set retry and exception boundaries.
- Test incomplete inputs and rejected outputs.
- Verify business value and repeated use.