Field guide / AI workflow automation
AI workflows make models useful inside real operations.
An AI workflow turns a model call into a repeatable part of the job. It defines the trigger, trusted context, model task, tools, review points, fallback, and record of what happened.
Working model
A production AI workflow
Treat the model as one component inside a system with explicit inputs, gates, and evidence.
- 01 / Trigger
A real event
Lead arrives, usage changes, meeting ends, renewal nears.
- 02 / Context
Trusted evidence
Fetch only the knowledge and records needed for the job.
- 03 / Reason
Bounded AI task
Classify, extract, compare, draft, or recommend.
- 04 / Control
Policy and review
Check confidence, permissions, risk, and exceptions.
- 05 / Act
Tool execution
Route, update, notify, or prepare the next action.
- 06 / Evaluate
Outcome feedback
Record edits, errors, adoption, and business result.
01
A prompt is an instruction. A workflow is an operating contract.
A workflow defines when the task runs, which data it may use, how tools behave, what happens when evidence conflicts, and who owns the result. The prompt covers only the model's part.
Write down the trigger, accepted inputs, required context, model task, permitted actions, review rule, output format, fallback, owner, and evaluation method before choosing a framework. This catches missing owners, unsafe actions, and undefined failure paths before implementation.
02
Match autonomy to consequence and reversibility.
Extraction and classification can often run automatically when inputs are constrained and errors are easy to reverse. Customer-facing messages, pricing changes, account reassignment, and other consequential actions need stronger checks.
Use a simple control ladder:
- Assist: the system gathers evidence and drafts; a person decides and acts
- Approve: the system prepares an action; a person accepts or rejects it
- Act with limits: the system executes only inside explicit thresholds and permissions
- Escalate: uncertain, novel, or high-impact cases move to a named owner
Choose the highest level of autonomy that keeps errors visible, reversible, and owned.
03
Evaluate the workflow at three levels.
Evaluate output quality, workflow health, and business impact. An accurate response that arrives late, reaches the wrong person, or creates unused work still failed.
Keep the scorecard small and diagnostic:
- Output quality: factuality, completeness, policy compliance, and useful format
- Workflow health: latency, failure rate, review rate, exception rate, and cost
- Operational value: adoption, time saved, decision quality, conversion, retention, or expansion
04
Use AI where company context changes the answer.
An inbound workflow can combine form data, enrichment, territory policy, open opportunities, and product fit before it recommends a route. A customer success workflow can combine usage changes, support conversations, commercial history, and the current success plan before it prepares a risk review.
Both jobs depend on current company knowledge and a record of the customer relationship. Retrieve only the context needed for the task, cite it when verification matters, and keep it separate from the model's general knowledge.
05
Design the failure path before the happy path ships.
Missing inputs, API timeouts, conflicting records, and model drift need visible failure states that an operator can inspect.
Define what the system does when evidence is weak, which actions are idempotent, how retries avoid duplicates, where an operator sees failures, and how a bad version can be disabled. Store enough structured evidence to reproduce the decision without retaining more sensitive data than the job requires.