Signal
A reliable event or request
Agentic AI decision guide
Agent or workflow automation?
A decision framework for choosing rules, integrations, human review, or agentic behavior without turning every workflow into an experiment.
- For
- Leaders comparing AI agents with integrations, rules, and existing workflow tools.
- Problem
- Teams can now add a model to almost any workflow, but the presence of an LLM does not make a route better. The wrong level of autonomy adds cost, ambiguity, and a harder failure path.
- Useful outcome
- Use a repeatable decision table to choose the least complex system that can achieve the required outcome.
The route
Match the system to the decision.
The right choice depends on input variability, action risk, context requirements, and how easily a person can verify the result.
Decide
Rule, model, or human judgment
Execute
A bounded system action
Recover
Exception, owner, and audit trail
Workflow context: Forms / CRM / Email / APIs / Human review
Start with the decision, not the label.
Write the step as a decision someone on the team already makes. Then ask whether the inputs are stable, whether the output must be exact, and whether a wrong action can be reversed. Those answers usually narrow the design before a vendor or model is chosen.
A rule is usually right when the trigger, conditions, and action are predictable. An agent may be useful when the system must interpret varied context, choose among approved tools, or draft a next step for review. Human work remains the right system when the consequence is high and the evidence is difficult to verify.
Use a simple choice table.
Choose deterministic automation for routing, synchronization, reminders, and calculations. Choose an agent for bounded classification, context gathering, drafting, or tool selection where the team can inspect the output. Choose a human decision when the workflow involves legal, financial, safety, or relationship judgment that cannot be reduced to a reviewable signal.
The goal is not to maximize autonomy. It is to make the next action dependable enough that the team can measure the route and recover when it fails.
- Stable input + exact output: rule or integration.
- Variable context + reviewable output: bounded agent.
- High consequence + unclear evidence: human decision.
Design the fallback before the happy path.
Every route needs a missing-data path, a duplicate path, an access failure path, and a person who owns the recovery. If the proposed system has no answer for those cases, the team is buying a demo rather than an operating capability.
Test the route against one normal case, one incomplete case, and one adversarial or ambiguous case. Keep the examples with the implementation record so the behavior can be checked after a model or tool changes.
Check your business readiness
WebMCP & AI Agent Readiness Audit
Check whether your website, systems, transaction path, fulfillment, and verification can support reliable AI agent access.
Reference material
Start with the platform documentation.
This field note is an educational guide. Platform behavior, availability, permissions, and plan limits should always be checked against the current vendor documentation.