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A private model. An explicit execution boundary.
Give model providers and enterprise delivery teams a way to separate model quality, business authority and evidence of what the tools actually did.
Reference integration scope; joint runtime qualification required
Private AI delivery / Retail operations / Enterprise platforms
The task is useful. The failure is specific.
The business task
A dedicated model forecasts demand and an agent prepares a replenishment draft. The customer wants to keep its own supplier rules, warehouses, quantity limits and approvers. A model upgrade should not silently change those authorities.
Where a normal flow becomes risky
A well-trained model can still propose the wrong operation. A private inference endpoint does not determine who may write to the ERP. Conversely, execution controls cannot make an inaccurate forecast correct. These responsibilities must be tested separately.
Reference workflow, not a claim of installed coverage
Make each handoff explicit.
- 01
Propose
The model or agent prepares a draft using the permitted task data. Evaluate forecast and task quality in the model provider's own evaluation process.
- 02
Describe
Map the proposed tool operation into a reviewed, versioned action-effect profile. Unsupported business meanings remain unsupported.
- 03
Decide
Apply the customer's current rules and approval ownership independently of the model's self-description.
- 04
Execute
Send through the qualified adapter to the business system, retaining its native access, validation and transaction controls.
- 05
Verify
Compare resource evidence with the requested task; separate draft creation, acceptance, commitment and whole-task completion.
| Situation | Observable change | Required handling |
|---|---|---|
| Bad forecast, valid operation | The model predicts demand incorrectly but the draft obeys the configured rules. | Record a model-quality issue. Execution governance is not prediction insurance. |
| Approved draft, changed warehouse | A later tool call changes the target or quantity. | Recheck the final operation against its scope before the resource write. |
| Tool upgrade, same valid JSON | A default, relationship or business meaning changes while the schema still accepts the fields. | Reassess the affected semantic profile and consumers rather than trusting syntax alone. |
| ERP writes, agent reports failure | The resource created the draft but the caller cannot confirm it. | Use native identity/readback and preserve uncertainty instead of creating another draft. |
Deployment and responsibility
Keep your systems. Define the missing contract.
What stays with you
Keep the chosen model, private inference service, agent runtime, model-quality verifier and business application. No model-provider exclusivity or new foundation model is required by this integration design.
What the integration adds
An explicitly scoped action adapter, runtime authority and recovery handling, and supported semantic evidence shared by execution and audit consumers. Define support, licensing and responsibility at each boundary.
| Boundary | Required information or capability |
|---|---|
| Partner interface | A real tool/write interface, the actual inference/runtime deployment, and an owner for each part of the delivery. |
| Customer controls | Customer-authorized identities, business rules, resource credentials and non-production acceptance data. |
| Data flows | Document inference, governance, tool, telemetry and support locations separately. Private inference alone is not an end-to-end data-residency guarantee. |
Designed for partners delivering agents that write to enterprise systems. For a read-only prediction or reporting project, runtime execution governance may not be the immediate customer need.
Observed, not assumed
What the evidence supports today.
A separate known-family Ambit replication produced 936 records and 1,872 independently implemented JavaScript/Python receiver outputs, with no false supported claims or role regressions. Complete point-to-point and generic-IR comparators matched its classifications.
Measure useful work and operating cost
Agree what success means before the pilot.
| Area | Evidence to collect |
|---|---|
| End-to-end delivery | Install the actual model/runtime, map one write path, test the resource result and exercise a real failure/recovery journey. |
| Interoperability | Add a second consumer and change the tool version; measure mapping errors, reassessment scope and actual maintenance work. |
| Commercial responsibility | Agree software and service scope, support ownership, deployment limits and exit requirements before any OEM or resale commitment. |
Use the same task, permissions, resource guarantees and failure schedule for the existing platform and proposed integration. Denial alone is not success: normal work and safe recovery must also remain usable.
Start with one boundary
Bring a workflow. Leave with acceptance criteria.
Select one task, its actual tools and the decision owner. We can assess whether existing controls are sufficient, whether a narrow adapter is useful, or whether a broader integration deserves a pilot.
Pilot scope, prerequisites and commercial terms are agreed separately. No production access is needed for the initial discussion.