INVENTORY
You know what AI you have and what it does
- Living inventory of agents
- Knowledge bases and integrations
- Per-agent keys, anti-impersonation
GOVERNANCE OF AGENTIC AI
Govern all your agentic AI.On any framework and model
Regentic.AI for Governance is the control plane for your entire fleet of agents, your own and third-party. It connects them without rewriting them, traces what they do and leaves evidence for the AI Act and the GDPR
Many agents. Nowhere to govern them
Fragmentation
Agents from different providers, each in its own system, with no common point.
Opacity
What an agent does ends up in logs that only a specialist can read.
Regulatory risk
Without traces or evidence, demonstrating AI Act compliance is not feasible.
Opaque cost
Model consumption grows with no control by agent or by use case.
What you get. All your AI, under one control
CONTROL
Rules that enforce themselves
- Deterministic engine, no LLM at run time
- Thresholds for cost, latency and sensitive data
- First warn, then block
EVIDENCE
Compliance backed by proof, not paperwork
- AI Act evidence from real data
- Reports sealed with SHA-256
- Explained in natural language
You cannot governthe AI you cannot see
Agents from different providers, each in its own system.
Regentic.AI puts them under a single control plane.
Connect. Observe.Govern.Four modules,a single control plane
How it works
It integrates through each agent's own channel, without touching it. Open standards: no vendor or protocol lock-in.
Your agents · on any framework and model
Frameworks
- LangGraph
- CrewAI
- LlamaIndex
- MS Agent Framework
- OpenAI SDK
- Google ADK
- Claude SDK
LangGraph
CrewAI
OpenAI SDK
Claude SDKCloud
- Bedrock AgentCore
- Vertex AI
- Azure AI Foundry
Bedrock AgentCore
Vertex AI
Azure AI FoundryLow-code
- n8n
- Copilot Studio
- Dify
- Make
- Zapier
n8n
Copilot StudioSaaS suites
- Agentforce
- ServiceNow AI Agents
- SAP Joule
Agentforce
ServiceNow AI Agents
SAP JouleModels
- GPT
- Claude
- Gemini
- Llama
- Mistral
- + your own agents
GPT
Gemini
Llama
MistralConnects through
- OpenTelemetry
- MCP
- REST API
- Python
- A2A
OpenTelemetry
MCP
REST API
Python
The control plane
01 · Telemetry
Telemetry
OTLP traces with GenAI semantics. Graph and waterfall flow. Alerts and incidents.
02 · Registry
Inventory
Agents, knowledge bases and integrations. API keys per agent.
03 · Governance
Governance
Deterministic rules for cost, latency, errors and sensitive data. AI Act profiles.
04 · Analytics
Analytics
Dashboard, costs, ranking and evaluations. SHA-256-sealed reports.
Each profile gets what it needs · in natural language
Management
Dashboard, costs and agent ranking
DPO and compliance
AI Act evidence and sealed reports
Technical teams
Traces, alerts and incidents
Measurable quality
Each agent, evaluated on five dimensions against its real execution.
- 01
Task success
- 02
Grounding in sources
- 03
Correct tool use
- 04
Personal data leakage
- 05
Policy adherence
Score from an LLM judge over a sample of real telemetry, not over lab tests.
Evidence built inwith every operation
No parallel records or hand-made reports.
Traceability comes from what your agents already run,
explained in natural language for management and the DPO.
EU AI Act: evidence from real data. Not from parallel records
- Art. 12
Event logging
Traces of every execution, captured natively with OpenTelemetry.
- Art. 13
Transparency
System purpose declared and linked to each agent.
- Art. 14
Human oversight
Oversight mechanism declared, traced and auditable.
- Art. 15
Accuracy and robustness
Continuous measurement on five dimensions and alerts on deviation.
The platform provides the evidence. Risk classification is assisted and is declared by the organisation: it is not an automatic legal determination.
Who it is for, and where
Who it is for
Agentic AI at scale
Companies and public administrations with multi-agent, multi-technology and multi-vendor AI.
Deployment
SaaS in Europe
Azure infrastructure (Western Europe). With your organisation's brand (white-label).
How we start. First observe, then enforce
1We take inventory
A living census of agents: without it no policy can be applied
2We connect and observe
Open standards: integration is configuration
3We govern
Rules that enforce and block, with AI Act evidence