April 2026 Public Draft
A Deterministic Control Plane for Enterprise AI Agents
Why enterprise AI agents need identity, authorization, retrieval boundaries, approvals, and evidence outside the model.
Akretic is built for security, AI platform, and infrastructure teams moving internal AI assistants from pilot to production.
Buyer Focus
Built for the teams taking internal assistants into production.
The initial workflow is an internal research or knowledge assistant: approved internal documents, controlled public-web fetch, and clear evidence about what the assistant tried to read, call, write, and send.
Identity and authorization evaluated outside the model
Permission-preserving retrieval before context reaches the LLM
Approval gates for sensitive reads and side effects
Egress checks before data leaves controlled systems
Tamper-evident evidence for review and investigation
Observation Mode before enforcement
Observation Mode
Start in non-blocking mode, then decide where to enforce.
Deploy Akretic in Observation Mode to observe assistant behavior without disrupting the current pilot path. Akretic records policy decisions and evidence outside the model so security and platform teams can review the control surface before turning on enforcement.
Policy decisions
Risky reads
Tool intents
Egress attempts
Approval candidates
Signed evidence
Credibility Boundary
Akretic constrains agent behavior. It does not claim to make AI models deterministic or eliminate all AI risk.
Framework mappings are planning inputs for pilot scoping, not certification, legal attestation, or compliance status.
Scoped Technical Addendums
Deep technical addendums, deployment-specific diagrams, and NDA-only security materials are not published as public downloads.
Request an Observation Mode Assessment to begin the review path and scope any non-public materials under the appropriate MNDA.