What are agentic workflows? Examples, architecture and when to use them
Learn how agentic workflows combine model decisions, external tools and human controls, with practical examples and a framework for choosing when they fit.
By Scrollport
In brief
An agentic workflow gives an AI agent a goal, a bounded set of tools and rules for deciding the next step from new evidence. It fits work where inputs vary and the route cannot be fixed in advance, but the outcome, authority and human review boundary can still be defined clearly.
An agentic workflow gives an AI agent a goal, a bounded set of tools and rules for deciding what to do next from the evidence it receives. It is useful when the route varies from case to case, but the outcome, authority and review boundary can still be defined clearly.
An agentic workflow chooses its next step
A fixed automation follows a route designed in advance. An agentic workflow can inspect a result, decide whether it is sufficient and choose another permitted step. The model makes that bounded decision; tools retrieve current information or perform the external action.
This does not require maximum autonomy. A useful workflow may make only one or two choices before returning evidence to a person. The important distinction is that the next step can depend on what happened during the run.
Agent, workflow or fixed automation?
| Pattern | Use it when | Example |
|---|---|---|
| Fixed automation | The route and rules are known in advance. | Copy every approved form submission into a CRM. |
| Agentic workflow | The route varies, but the job and permitted actions are bounded. | Research a market, resolve evidence gaps and produce a sourced brief. |
| Open-ended agent | The person wants an ongoing assistant across many different jobs. | Help an operator plan, research and prepare work throughout the week. |
Use the least autonomous pattern that can handle the variability. If a flowchart can describe every route, conventional automation is normally easier to test and operate.
The architecture of an agentic workflow
- Outcome: define the useful result and how a person will judge it.
- Context: provide the constraints, source boundaries and existing material.
- Tools: permit only the external capabilities the job may require.
- Decision loop: let the agent select the next permitted step from new evidence.
- Controls: set spending, approval, access and stop conditions before execution.
- Evidence: retain sources, tool results and assumptions with the final output.
Scrollport supplies the tool-access part of this architecture. The agent starts with five stable controls: list the apps available to it, discover the relevant capability, inspect one current tool and its price, run it, and check the wallet boundary. The agent’s own harness still owns planning, memory, context and the wider workflow.
This search-and-load pattern avoids placing an entire catalog in the agent’s context. It also keeps the literal tool choice inspectable at the point where it can change the outcome. Read why Scrollport uses a small control surfacefor the underlying design.
Business workflows that fit
- Research: choose sources, close evidence gaps and produce a cited brief.
- Prospecting: find matching companies, enrich only useful fields and verify contacts.
- Competitive analysis: collect comparable public data, classify patterns and preserve provenance.
- Content production: turn approved research into a structured script, image, audio or presentation.
These jobs combine variable inputs with a reviewable result. They are better starting points than broad instructions such as “run marketing” or “manage sales”, where authority, success and accountability are too vague.
Design for control and evidence
Decide who owns the business outcome before deciding how autonomous the agent should be. That person defines the acceptable sources, approval points, spending boundary and conditions that require escalation. Reversible research can usually run with more freedom than publishing, purchasing or changing customer data.
Evaluate the completed job, not just the fluency of the answer. Check whether the agent chose an appropriate tool, used relevant evidence, stayed inside its authority and produced a result the next person could act on. IBM’s agentic workflow overview and AWS’s agentic AI patternssimilarly treat tools, control flow and oversight as parts of the system rather than model features alone.
Start with one bounded outcome
Choose one frequent job with variable inputs, a clear owner and an output you can review. Run it manually with the agent first. Record wrong tool choices, missing context, weak evidence, unnecessary calls and every point where a person had to intervene. Those observations define the workflow better than an abstract architecture diagram.
When the job genuinely needs external capabilities, browse the live tool catalogor follow the quickstart. Keep the outcome in the prompt and let the agent discover and inspect the implementation at runtime.