The Authority Control Plane
for the AI workforce
Six purpose-built pillars that define, enforce, and audit every decision your AI agents are authorized to make — across every runtime and every system.
The AI Workforce
Every non-human worker, governed under one authority model
The Control Plane
Six pillars, one platform
From the Authority Registry that records what every agent is empowered to decide, to the Orchestration Engine that contains a breach in seconds — every pillar is purpose-built for agentic AI.
Authority Registry
The system of record for every AI worker: profile, owner, purpose, delegated authority, risk tier, lifecycle state, and trust score.
Decision Policy Engine
Structured, machine-readable policies that translate organizational mandates into allow, escalate, block, or require-approval outcomes.
Runtime Evaluation Engine
Every API call, tool use, data access, and command evaluated in real time against the agent's authority — before it executes.
Trust Engine
A continuously updated trust score for every agent, derived from decision quality, policy compliance, historical accuracy, override rate, and confidence.
Decision Ledger
Tamper-evident, immutable audit trail of every decision, approval, escalation, denial, override, policy evaluated, and human involved.
Orchestration Engine
Automated containment and remediation: rotate, revoke, quarantine, kill-switch, ticket, and notify the systems that already run your business.
Authentication & Identity Context
Botaris doesn't replace IAM — it governs above it
Identity providers and secrets managers answer who the agent is. Botaris answers what it is authorized to decide, building on the identity context they already provide.
Cloud Providers
AWS, Azure, Google Cloud
Identity Providers
Okta, Entra, Ping, SailPoint
Secrets & PAM
CyberArk, HashiCorp Vault, AWS/Azure
Kubernetes
EKS, GKE, AKS, OpenShift
CI/CD & DevOps
GitHub, GitLab, Jenkins, Azure DevOps
Agent Frameworks
LangChain, CrewAI, Semantic Kernel, MCP
Model Platforms
OpenAI, Anthropic, Gemini, Bedrock, Azure AI
Enterprise Systems
Salesforce, SAP, Workday, ServiceNow
Continuous Governance Lifecycle
Discover → Authorize → Enforce → Prove
A complete lifecycle for every AI worker, from first sighting to audit-ready evidence.
- 01
Discover
Find every AI worker operating in your environment.
- 02
Register
Capture identity, owner, environment, and context.
- 03
Assign Purpose
Define what the agent exists to do.
- 04
Assign Authority
Grant scoped, versioned authority with expiry.
- 05
Evaluate Decision
Real-time policy evaluation on every action.
- 06
Monitor Behavior
Continuous behavioral and policy monitoring.
- 07
Detect Risk
Flag drift, anomalies, and authority breaches.
- 08
Enforce Policy
Allow, block, escalate, or require approval.
- 09
Log Decision
Write every outcome to the Decision Ledger.
- 10
Generate Evidence
Audit-ready reports for regulators and the board.
Cross-Cutting Services
Enterprise-grade by default
Outcome
A Trusted Autonomous Workforce
Every AI worker has an identity, an owner, a purpose, and defined authority — and makes only authorized decisions, with complete accountability for every one of them.
Request early access- Has an Identity
- Has an Owner
- Has a Purpose
- Has Defined Authority
- Makes Only Authorized Decisions
- Is Continuously Monitored
- Produces Complete Accountability