Enterprise Agentic AI Architecture Blueprint
Reference design for discussion. Adapt to the use case, selected products, data requirements and customer controls. This is not a deployed solution or a compliance certification.
TECHTIERA · ARCHITECTURE REFERENCE
A starting point for designing an AI workflow with approved data access, controlled tools and accountable operational ownership.
The execution path
When evidence is missing, a control fails or an execution limit is reached, stop the workflow or route it to the defined exception process. Human review should receive the relevant evidence and action proposal.
Architecture layers
| Layer | Design decisions to document |
|---|---|
| Business workflow & ownership | Objective, KPIs, business owner, permitted actions, approval policy and stop conditions. |
| Context & retrieval | Approved sources, access filtering, freshness, citations, retention and deletion handling. |
| Agent orchestration | Task state, single-agent or specialist roles, checkpoints, bounded retries and fallback paths. |
| Tool execution boundary | Typed inputs, operation allowlists, authorization, transaction limits and idempotency. |
| Human review & exception handling | Review interface, evidence, escalation queues, approval records and controlled resumption. |
| Identity & secrets | Least-privilege service identities, token validation, secrets management, rotation and revocation. |
| Evaluation & releases | Representative datasets, adversarial cases, regression tests, acceptance gates and rollback. |
| Operations & observability | Task outcomes, tool failures, drift, latency, token cost, incident ownership and runbooks. |
Example: AP exception review
Retrieve the invoice, purchase order and receipt through approved interfaces. Compare the evidence, identify the exception and prepare a resolution. Enforce duplicate checks and amount limits outside the model. Route external communications and financial changes through the approvals agreed for the workflow. Record the outcome and reconcile any approved update.
Release and operational review points
Before a pilot
Select one bounded workflow. Confirm source and system access. Define the success metric, failure cases and manual fallback.
Before production
Validate identity and action controls, data handling, reference evaluations, recovery, approval behavior and support ownership.
After go-live
Review incidents and changed APIs/models. Re-evaluate releases, refresh knowledge, revoke unused credentials and maintain the backlog.
What to bring to a discovery session
- The workflow and a representative set of successful and failed cases.
- Systems, data sources, interface documentation and access constraints.
- The actions the AI may recommend or execute, and the approval authority.
- Quality, latency, cost, hosting and support requirements.
