Real enterprise work · Practical guide
How do you build an agentic workflow for your enterprise?
Choose a bounded business outcome, map the decisions and handoffs, and connect an agent only where interpretation or judgment helps. Keep permissions and business rules in enforced controls, preserve workflow state outside the conversation, and test complete cases before extending authority. Start with work such as preparing a supplier onboarding pack, rather than a general instruction to automate procurement.
For Business workflow owners, operations teams, enterprise architects and engineers who need people and agents to complete work together.
A design guide with fictional worked examples. It does not report an executed implementation or customer result.
An agentic workflow combines model-assisted work, tools and accountable decisions. A procurement agent might read documents, identify missing information and prepare a review. The analyst needs to know which records were used, what changed and who approves it.
Not every step needs an agent. Anthropic distinguishes predefined workflows from agents that decide their next actions. Use ordinary code for exact calculations, required fields and permission checks. Let a model interpret inconsistent documents or suggest the next permitted step when that flexibility is useful. More agents do not automatically make the work better.
What you need before you build
- An agreed finish line
- For a first project, define success as a complete, evidence-linked onboarding pack ready for an authorized reviewer. Creating a payable supplier in the live ERP is a separate authority.
- Representative enterprise material
- Obtain approved sample forms, policy versions, document examples and supplier records. Include missing, contradictory and expired material. Use synthetic or appropriately de-identified data where required.
- Tools and responsible owners
- Name the business owner, integration owner and reviewer. Confirm document storage, supplier-system interfaces, identities, licences and a test environment. A connector being available does not establish permission to use every record.
Build the agentic workflow
1. Describe one case from request to review
Record the trigger, documents, decisions and final artifact. Observe an analyst handling incomplete forms and conflicting bank details. Include those exceptions in the design. Establish how the current process is assessed for a meaningful comparison.
2. Separate rules, interpretation and authority
Mark each step as a deterministic check, a model-assisted interpretation or a human decision. A model can extract a registration number and cite its source. Code can compare it with the submitted form. A procurement reviewer decides whether a discrepancy is acceptable. Do not turn an agent's confidence statement into authorization.
3. Specify small, explicit tools
Define tools such as read_document, find_supplier and save_review_draft with narrow inputs and outputs. Keep record access checks in the service that reads or writes the data. Return explicit outcomes such as not_found, access_denied and temporarily_unavailable. Do not present an integration error to the agent as an empty supplier record.
4. Store durable case state
Give each case an identifier and explicit states such as received, needs_information, ready_for_review and approved. Store source versions, findings, reviewer decisions and the last completed action outside the model conversation. Before resuming, check whether source material or approval has changed. Repeated delivery of the same event must not create another case or repeat a write.
5. Design the review and exception handoff
Show the reviewer the proposed action, supporting evidence and unresolved discrepancies. An approval should apply to a specific case and proposed change, not every future action. Define who receives missing-information requests, how a case waits, and when it expires. Rejection and cancellation are normal outcomes that the workflow must support.
6. Assemble the smallest complete implementation
Choose an approved orchestration service or application framework. Connect a document source, limited supplier lookup and review screen in a controlled environment. Add authentication, event recording and bounded retries. Introduce specialist agents only when separate responsibilities improve a measured result.
7. Test cases against agreed expected outcomes
Have the business owner define expected findings and permitted state changes before running the cases. Check both the final pack and the sequence of actions. OpenAI's evaluation guidance connects workflow traces with repeatable datasets: use that distinction to investigate a failure, then preserve it as a regression case. A good summary cannot compensate for an unauthorized write.
8. Decide what can move into operation
Review failures, exception workload, completion time and reviewer corrections alongside output quality. Agree the next scope, monitoring, rollback and support owner. A production decision also needs the organization's security, integration and operating acceptance. Re-run affected cases when tools, policies, prompts or models change.
Illustrative worked example
Worked design: a supplier onboarding review pack
The workflow prepares a supplier onboarding pack for an authorized reviewer.
| Case material | Workflow output | Required next decision |
|---|---|---|
| Form and registration document agree | Draft pack with field-level evidence and completed checks | Reviewer approves the pack for the next operating step |
| Form names a different account holder | Discrepancy flagged with both source references | Authorized owner resolves the conflict; no bank update |
| Required certificate is missing | Case waits for information with a clear missing-item request | Requester supplies the document before review resumes |
| Supplier lookup times out | Retryable integration failure recorded against the same case | System retries within limits or alerts the integration owner |
Assess the review pack and case history, not just the fluency of the agent’s summary.
What to check
- Evidence survives interpretation
- Every material extracted value links to the approved source and version. Unsupported conclusions or unreadable documents remain explicit gaps.
- Authority is enforced at the tool
- Attempt an unauthorized record lookup and a write without approval. Both must fail even if the model requests them. Document text is input evidence, not permission to change workflow rules.
- Restart and duplicate events are safe
- Interrupt the run after a saved draft, resend the trigger and resume it after a policy change. Verify one case, no duplicate side effects and renewed review where needed.
- Exceptions reach a person
- Test conflicting documents, unavailable reviewers and exhausted retries. A trace should explain what stopped, who owns the next action and what remains unresolved.
Make this real in your enterprise
A bespoke Nuvepro engagement starts with your actual onboarding work and agreed enterprise context. Your team builds or configures a bounded workflow and demonstrates it against representative cases.
For the onboarding project, agree who will configure or build each part and how their work will be checked. A procurement analyst might configure review rules while an engineer implements state recovery and system access; the split depends on the team’s experience. Confirm which interfaces are connected and which are simulated. You can also bring an existing agent for separately scoped evaluation without a team-training programme.
Scope and limitations
- A simulation represents agreed conditions. It cannot establish compatibility with every live enterprise system, resolve data rights or replace production approval.
- Model, orchestration and connector choices depend on enterprise requirements. This guide describes a design approach, not runnable integration code or a guarantee of autonomous completion.
Questions about this project
What is the difference between an AI agent and an agentic workflow?
An agent chooses actions within its tools and instructions. A workflow coordinates the larger case, including fixed checks, people and systems. A workflow can include an agent without giving it authority over every step.
Can we use our existing ERP or workflow platform?
Check supported interfaces, permissions and test access. Reuse approved tools or simulate an interface when live integration is outside scope.
How do we know the workflow is ready?
Agree representative cases, expected outputs, permitted actions and human acceptance criteria. Review the results and remaining failures. Readiness evidence is specific to what was assessed; deployment is a separate owner decision.
Sources and further reading
Check current product support, permissions and licences for your project. References do not imply a Nuvepro partnership or available connector.
- Anthropic: Building effective agents
Architecture guidance distinguishing predefined workflows from agent-directed action. It does not establish that more autonomy improves every business process.
- OpenAI: Guardrails and human review
Documents tool validation, approval interruptions and resumable state. Application controls still need to enforce the enterprise's actual authority rules.
- OpenAI: Evaluate agent workflows
Explains traces, workflow grading and repeatable evaluation datasets. These tools do not supply the customer's acceptance criteria.