Black-box behavior
When a customer complains, the team cannot reconstruct what the agent saw, what it decided, and why. The session is gone the moment it ends.
AI agent observability
Audit-grade observability for AI agents.
AI agent observability and audit trail in one. Tamper-evident records of every prompt, decision, and action, structured for conduct reviews, complaints, and regulator audits.
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Trusted by teams securing AI in production
What audit teams hit without AI observability.
AI agents without first-class audit produce the same pain in every review cycle. Reconstruction replaces evidence.
Logs that do not stand up
Plain-text transcripts and ad-hoc logs are not enough for conduct reviews or regulator-grade evidence. Integrity and completeness cannot be proven.
Multi-week reconstruction
Each audit or complaint becomes a project. Engineering pulls logs, ops correlates events, and the answer arrives weeks after the question.
SIEM blind to agent actions
Your security stack sees servers and services. It does not see what the agent decided, what it called, or what it returned to the customer.
Audit trail
Prompt classified
09:24:01 · intent: refund · risk 0.12
Tool call decided
09:24:01 · refund.issue · escalated
Output redacted
09:24:02 · 2 PII fields removed
Record signed
09:24:02 · tamper-evident · chained
AI audit trail
A full audit trail of what happened.
Every prompt, classification, decision, action, and output is captured as a structured, tamper-evident AI audit trail. A complete trace of what each agent saw, decided, and did, ready for review without rebuilding context.
Session replay
Customer #4821 · refund dispute
3m 12s · 14 events captured
Decision: refund escalated
policy v14 · approver: ops
Output: PII redacted
2 fields removed before send
Replay full session
Conduct-ready replay
Reconstruct any session in minutes.
LLM observability with conduct-ready replay: pull up any conversation, any decision, any action, with the full context the agent operated in. Reviewers, complaints teams, and risk all see the same evidence on day one, not weeks later.
Evidence pack
Complete interaction record
prompt, decision, action, output
Tamper-evident & attributable
signed · chained · exportable
Reviewer, risk & compliance
one shared source of truth
SOC 2ISO 27001DORA
Built for reviews and regulators
One source of truth for reviewers and regulators.
When a conduct review, complaint, or regulator request lands, the record is already there: complete, attributable, and consistent. Reviewers, risk, and compliance all work from the same evidence instead of reconstructing it after the fact.
Safe and customizable, without compromises.
Keep your data E2E encrypted
Protect agent workflows with end-to-end encryption, real-time redaction, and policy checks that block unsafe behavior in milliseconds while approved work keeps moving.
Policy-driven security
Define how agents handle data, tools, and decisions once. Averta applies those rules across every prompt, response, and action.
Adaptive data controls
Tune policies by team, use case, customer state, risk level, and tool permission without hardcoding guardrails into every agent.
What security teams are saying.
Before we started using Averta, we were hesitant to share sensitive information with agents. Averta changed that by providing the security and trust we needed, allowing us to significantly enhance our customer service experience.
Amir HaleemFounder at
HeliumThe decision layer in front of every action.
Classification, policy, access control, and audit working together as one AI agent security platform, protecting your agents internally and in production.
Classification Engine
Score every prompt for risk.
AI guardrails that score every prompt, tool call, and output for intent and risk before your model acts.
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Tool Policies Framework
Govern every tool call.
AI agent governance: define what each agent is allowed to do, enforce it on every tool call, attribution included.
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MCP Gateway
Govern MCP tool access.
Expose only approved tools to each AI agent, through one governed MCP gateway.
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Averta Red Teaming
Pressure-test your agents.
Adversarial campaigns that simulate prompt injection, tool abuse, and data exfiltration on your production agents.
Read moreAudit and observability, specifics.
What conduct, risk, and security teams ask before signing off on the audit layer for production agents.
LLM observability is the ability to see, replay, and audit what a large language model and its agents did at runtime, including prompts, tool calls, decisions, and outputs. For AI agents, it means a structured record of every interaction so engineers can debug it and reviewers, risk, and regulators can audit it.
Two years by default, configurable up or down per data class to match your retention obligations. Records remain searchable and exportable through the full retention window.
Structured, machine-readable events covering every prompt, classification, decision, action, and output. Each record is exportable as JSON or CSV through the API.
Yes. Each record is signed and chained, so any modification is detectable. Integrity and completeness can be proven to a reviewer or regulator.
Yes. Every record exports as JSON or CSV through the Dashboard. From there you can retain it, analyze it, or load it into your own SIEM or data lake.
The audit layer produces the evidence these frameworks expect: complete, attributable, tamper-evident records of every agent action, retained and exportable on demand.
Yes. Any agent, whether an internal copilot, a back-office automation, or a customer-facing assistant, is captured the same way.
Dev observability tools track latency, cost, and output quality for engineers. Averta covers the same agent activity but produces audit-grade, tamper-evident records built for reviewers, regulators, and your own evidence systems. You can run both: one keeps the app healthy, the other keeps you defensible.
See Averta OS in action
Book a demo and see how Averta OS secures your AI agents from input to execution.
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