AI agents vs workflow automation: how to choose
Choose workflow automation when you can define the trigger, steps, and expected result in advance. Consider an AI agent when the request needs interpretation or the next investigation step depends on what the previous one found. An operations process can use both: flexible investigation followed by a tightly defined action.
What is the difference between an agent and a workflow?
Terminology varies across products. For this guide, a workflow follows paths defined in code or configuration, while an agent uses a model to choose its next step from the tools available to it. A workflow can contain AI steps, such as classifying a request, without giving a model control of the whole process.
This distinction follows Anthropic’s architectural description of workflows and agents. It is a way to reason about a process, not a feature comparison between vendors. For your own evaluation, inspect who chooses the next step and what constrains that choice.
Anthropic: building effective agents
Use a workflow for a known sequence
Suppose every completed import must produce a summary record and notify its owner. The event, required fields, destination, and failure handling can be specified before the import happens. A workflow lets you inspect those rules directly and test each branch against known inputs.
Document what happens when the owner is missing, a notification fails, or the same event arrives twice. These are process design questions regardless of whether an AI step appears anywhere in the sequence. Adding a model does not remove the need to answer them.
Use an agent for a variable investigation
Now consider “why is this customer’s setup still blocked?” The useful lookup depends on the first result. The customer might be waiting on a required field, a provisioning job might have failed, or setup might be complete while another record is stale. An agent can help navigate those possibilities through the tools you expose.
Define an acceptable output: identified customer, current status, evidence, unresolved questions, and a proposed next step. If the agent cannot find a reliable identifier or the sources conflict, asking for clarification or handing off the case can be the correct result. Evaluate that behavior alongside successful answers.
Combine investigation with a defined recovery action
An illustrative hybrid starts with a teammate asking about a failed setup. The agent reads permitted records and identifies a documented recovery candidate. The teammate then reviews a proposal to run one specific operation. A policy decides whether it can proceed and whether a designated person must approve it.
Keep the action’s contract explicit: accepted inputs, target environment, allowed resource scope, preconditions, and how completion is verified. The agent’s explanation helps someone assess the proposal; it should not create additional authority to execute it.
See how approval workflows govern an agent’s proposed action
Ask these questions before choosing
- Can we list the branches in advance? If so, start by evaluating a workflow.
- Does the request require interpreting free text or choosing among investigative tools? Test an agent on representative cases.
- Can we verify the answer? If the evidence is unavailable, improve access to the source before expanding automation.
- What can the process change? Define authorization and approval independently of the choice of interface.
- How does it stop? Name the conditions for completion, clarification, failure, and human escalation.
Compare the whole cost of handling a case
Evaluate setup effort, ongoing maintenance, response time, review effort, and the cost of incorrect outcomes. For an agent, also record tool calls and model usage per case. For a workflow, record exception handling and the work needed when a source changes. A low execution cost is less useful when every result requires a long manual check.
Use the same cases and acceptance criteria for both approaches where possible. Include incomplete input, unavailable sources, repeated requests, and out-of-scope records. Keep a stable evaluation set so a changed configuration can be compared against the previous version.
Where GlowForm fits
GlowForm provides a conversation for investigating connected systems and preparing authorized operations. Administrators configure connections, publish tools, and grant agent policies. The planner can draft a change for a person to review; it does not grant itself permission or approve the change. For a scheduled sequence that already works, evaluate whether adding a conversational investigation path would solve a real gap.