01 · IDENTITY
Who is the agent acting for?
Bind authenticated subject, tenant and role context on the trusted side rather than accepting caller-supplied actor claims.
AGENTIC AI ASSURANCE
Before an AI agent acts, prove the action deserved to happen.
An agent can reason correctly and still act with the wrong identity, stale evidence, excessive authority, an unsafe tool path or duplicate intent. SHANX tests those action boundaries before consequential autonomy is trusted.
THE AGENT ACTION PATH
Can you reconstruct why the agent was allowed to act?
Agentic AI Assurance examines the operational boundary around an AI agent action. It does not replace model evaluation, security engineering, governance, ERP, CRM, WMS, MES or the organisation's accountable functions. It adds bounded evidence around one consequential action path.
02 · EVIDENCE
What does the agent actually know?
Check approved source, freshness, contradictions and missing dependencies before consequential intent is promoted.
03 · AUTHORITY
May this agent take this action?
Apply explicit authority and default-deny policy before tool execution is permitted.
04 · TOOL BOUNDARY
Where may the agent act?
Resolve destinations and operations through server-side allowlists instead of trusting arbitrary caller-supplied URLs or tools.
05 · REPLAY
Was this action already executed?
Recognise exact replay while rejecting materially changed intent that attempts to reuse an earlier action identity.
06 · OUTCOME
What actually happened?
Separate decision, execution and downstream outcome records so the agent cannot certify its own success by assumption.
SYNTHETIC EXAMPLE
An authorised tool call can still be the wrong action.
Imagine an AI agent handling a service exception. It identifies an account, reads CRM and billing state, and is allowed to issue a credit. Agentic assurance still asks whether the correct tenant was bound, whether both approved systems were fresh enough, whether they contradicted each other, whether the credit action had already been attempted, whether the tool destination was allowlisted, and whether an independent downstream receipt proves the credit actually completed.
Agentic-assurance rule: autonomy is not a permission to skip evidence. Preserve identity, evidence, authority, tool boundary, execution identity and independently observed outcome.
WHAT SHANX HAS ACTUALLY PROVEN
Agentic claims stay inside tested boundaries.
The current SHANX reference and managed-staging work supports bounded synthetic and approved read-only validation of several controls relevant to agentic systems. It is deliberately not presented as proof of unrestricted enterprise agent autonomy.
| Capability | Status | Evidence boundary |
|---|---|---|
| Managed synthetic user → JWT → protected Edge identity boundary | PROVEN | Authenticated subject, tenant context and role context reached a live managed identity boundary. |
| Server-controlled tenant and role context | PROVEN | Trusted-side identity context is used instead of caller-supplied actor fields. |
| Intent idempotency and replay protection | PROVEN | Exact replay is recognised while changed intent conflicts rather than silently reusing an earlier action identity. |
| Durable queue, lease ownership, retry and dead-letter behaviour | PROVEN | Reference and managed Postgres paths validated lease safety, retry exhaustion and dead-letter handling. |
| Server-side connector allowlisting and fail-closed faults | PROVEN | Unknown, disabled, hostile, DNS-failed, credential-missing and timeout cases failed closed in synthetic managed connector tests. |
| Tamper-evident audit and separated outcome verification | PROVEN | Reference evidence validates audit-chain integrity, tamper detection and post-execution outcome observation. |
| Production enterprise agent frameworks and real production tool credentials | NOT YET PROVEN | No blanket claim is made for production agent frameworks, enterprise applications or unrestricted live tool execution. |
| Enterprise IdP rotation/revocation, private network or mTLS, external penetration testing, HA/DR and SLA | NOT YET PROVEN | These remain separate readiness gates. |
For the wider control layer, see Enterprise AI Assurance. For the decision path, see AI Decision Assurance. For source provenance and freshness, see AI Evidence Integrity.
BOUNDED AGENT REVIEW
Start with one consequential agent action.
A first validation should be narrow enough to reconstruct completely: one agent action, one accountable owner, one approved evidence boundary, one tool boundary and explicit acceptance criteria.
01Select the action
02Bind identity + authority
03Map evidence + tools
04Test replay + failure
05Observe outcome
Typical first boundary: synthetic or explicitly approved read-only evidence, no production credentials by default, no live production writes, and no claim beyond what the resulting evidence establishes.
FAQ
Agentic AI assurance, in plain language.
What is Agentic AI Assurance?
It is the discipline of validating the operational evidence around an AI agent action: identity, evidence, authority, tool boundary, replay status, execution and downstream outcome.
Is this the same as AI agent governance?
No. Governance defines ownership, policy and acceptable use. Agentic assurance tests whether one actual action path follows those expectations under normal and failure conditions.
Why do tool boundaries matter?
Because an agent that may call arbitrary tools or destinations can escape the intended workflow boundary. Allowlisting keeps the action surface explicit and testable.
Why is idempotency important for agents?
Retries, timeouts and duplicate events can cause an agent to repeat a consequential action. Idempotency distinguishes a safe replay from a materially different new intent.
Does SHANX claim production-ready autonomous agent deployment?
No. Current evidence supports bounded reference and managed-staging controls. Production integrations, enterprise identity operations, private networking, external security testing, HA/DR and SLA remain separately gated.
BRING ONE AGENT ACTION
Customer asks. SHANX shows evidence.
If an AI agent could create a material customer, operational or financial consequence, send us one bounded action path. We will tell you what is already proven, what remains unproven, and what a useful validation would need to establish.