AI agent workflow
An agent is useful when it can inspect a situation, choose from a small set of actions, and leave a record. It still needs a process around it.
Plain definition
A normal automation follows fixed branches. If a form says Australia, assign the Brisbane queue. If an invoice total is above $5,000, request approval. Those rules are predictable and cheap.
An AI agent workflow adds a reasoning step when the input varies. The agent might read an enquiry, decide which approved research tools to use, compare the result with a scoring guide, and propose the next action. The surrounding workflow still handles identity, permissions, timeouts, logs, approvals, and writes back to the source system.
We use fixed rules whenever they can do the job. We add an agent only where language, incomplete context, or variable task order makes rigid branches costly.
Worked example 01
A lead agent can read free-text enquiries and gather missing business context. It should score against explicit criteria, cite the fields it used, and send uncertain cases to a person. We do not let a model invent the qualification policy while it runs.
Workflow map
New lead to owned next step
Capture the form, source, company, request, and consent fields.
Check fit against a defined market, service, and budget rubric.
AutomatedPrepare a reply or internal task from an approved template.
AutomatedA person reviews high-value leads before anything is sent.
Worked example 02
A support agent can classify messy requests, retrieve approved knowledge, and draft a response. Account changes, refunds, security issues, and low-confidence answers stop for a person. That boundary lets the fast cases move without hiding risk.
Workflow map
Ticket to resolution queue
Read the subject, message, account tier, and recent activity.
Assign topic, urgency, sentiment, and required skill.
AutomatedFind the relevant policy, help article, or account record.
AutomatedSend safe answers and route uncertain cases with context.
Worked example 03
A reporting agent can investigate unusual movements before it writes commentary. The useful output is not a polished paragraph alone. It is a short explanation tied to source metrics, with missing data and suspected tracking faults called out for review.
Workflow map
Raw metrics to reviewed report
Read agreed metrics from analytics, CRM, finance, and ads.
Flag missing dates, sharp changes, and mismatched totals.
AutomatedSummarise changes against targets with source references.
AutomatedApprove the commentary before the report is distributed.
Design rules
We define what the agent can read, which tools it can call, the actions it may propose, and the conditions that require approval. We also set a run limit so a failed job cannot loop through paid calls or repeated updates.
Start with 20 to 50 real examples. Measure correct routing, useful drafts, human edits, failure types, run cost, and time saved. If the workflow performs well, widen its authority one controlled action at a time.
For more concrete patterns, explore our AI workflow examples. If you need help choosing the right first process, see our AI automation consulting service.
Start with one workflow
We will test whether it needs an agent, a fixed automation, or a simpler process change.