Solutions · Agent Accountability

Non-human identity. Signed action chains.

Scoped permissions, tool-call audit, and human-approval gates. Every agent action signed. Every tool call traceable. The complete accountability surface for agentic AI — with the same rigor as human-originated decisions.

A human loan officer has a badge, a scope, a supervisor, and a paper trail. An AI agent making the same decisions typically has none of that. Veridra fixes it.

Why agentic AI needs its own accountability layer

When an agent acts autonomously — booking travel, issuing refunds, running code, executing trades — it does so with delegated authority. The question of whose authority, under what scope, with what approval chain, does not answer itself. Standard model-risk frameworks were not designed for agents; standard logging was not designed for evidentiary-grade action chains. The accountability gap is where enterprises are getting exposure as they scale agent deployments.

The four accountability primitives

Identity before action

Every agent has a scoped identity issued through your IdP — Okta, Entra, Ping — or SPIFFE-based service identity for cloud-native deployments. The identity carries declared purpose, scope, operator, and approval chain. No anonymous or shared-account actions.

Permission scope enforced at the boundary

Scopes are declared, not inferred. An agent authorized to read data is not automatically authorized to write or execute. Scope enforcement happens at the tool-call boundary — calls outside scope are rejected before they reach downstream systems, and the rejection is itself a signed event.

Every tool call is a signed decision

Tool calls are captured in the same Attest pipeline as human-originated decisions: canonicalized, signed with the tenant's key, logged to the transparency tree. The full context — agent identity, prompt, tool name, arguments, result — is preserved. Replayable. Forensically reconstructable. Admissible.

Human-approval gates where thresholds warrant

For high-impact actions, approval gates route to named humans before the action executes. Thresholds configurable per agent, per action type, per amount, per customer segment. Approvals and rejections are themselves signed, entering the evidence chain as first-class events.

What this prevents

Specific agent failure modes it addresses

Tool poisoning — agents called into executing actions they were not authorized for. Cascade failures — one agent invoking another without provenance. Prompt injection with privilege escalation — inputs designed to trick agents into out-of-scope actions. Silent action drift — agents gradually expanding what they do without governance awareness. Each of these is a current, documented failure mode in enterprise agent deployments. Each is addressable with accountability primitives at the infrastructure layer.

Framework alignment

EU AI Act high-risk obligations — agent-operated decisions in regulated domains inherit the same Article 9/12/14/15 obligations as human-operated decisions. Agent Accountability produces the specific evidence.
NIST AI RMF MANAGE-3 — third-party and delegated-authority risk. Agents acting on behalf of the organization need the same governance as third-party providers.
OWASP Top 10 for LLM applications — LLM02 (insecure output handling), LLM05 (insecure plugin design), LLM07 (insecure plugin design), LLM08 (excessive agency) all map to specific Agent Accountability controls.
SOC 2 CC6 (Logical Access) — agent identities are access-controlled subjects; accountability treats them as such.

When to deploy

The right moment for agent accountability

If your enterprise has already deployed agents in production, accountability is overdue. If you are planning to, build the accountability layer before scale. Retrofitting identity and approval to agents that were deployed without them is hard; designing agents around the primitives from day one is straightforward. Veridra's Agents module ships Q3 2026; design partner access begins Q2 2026.

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