Control what every AI agent can access, do and change
Govern every agent in production through one reusable control layer. Moring engineers assemble the AI Control Plane inside your environment around your identity, access and approval systems.
Every agent needs a governed path to production
Identity, model access, tools, approvals and monitoring often sit across different teams. Without a shared control plane, each new agent requires another set of integrations, permissions and production reviews.
Security reviews start again
Teams have to establish how another agent will access data, tools and systems.
Controls differ across teams
Identity, approval and monitoring are implemented differently for each agent or environment.
Activity is recorded without proving authority
Logs may show what happened without showing which policy allowed the action.
Govern the action before it reaches a real system
If a check happens after the action, it is evidence, not control.
One control layer across agents, models, tools and systems
Agent identity and authorization
Give every agent a distinct identity and evaluate what it may do in the context of each request.
Models, MCP servers and tools
Register approved models and tools, control how agents reach them and apply the same access policy across teams.
Human approval and delegated authority
Pause before consequential actions and give a named reviewer the evidence and scoped authority needed to decide.
Agent runtime and stop controls
Run agents within defined network, sandbox, tool and action boundaries, with limits and a way to interrupt or stop them.
Traces, evaluations and audit evidence
Capture policy decisions, tool calls, approvals, outcomes and evaluations in one reviewable record.
Usage, budgets and cost limits
Track usage by agent, workflow and team. Apply budgets, loop limits, throttling and action limits.
The AI gateway is one layer, not the whole control plane
An AI gateway sits in the request path between an agent and the model providers it calls. It handles the traffic problems: routing across providers, retries and fallbacks, provider keys, token-based quota and per-team spend, and inspection of prompts and responses.
That is a real and necessary layer. It is also a narrow one. A gateway sees a request leaving for a model. It does not record which user the agent was acting for, which policy version applied, which tool call followed the response, or who approved the action. It cannot stop an agent from writing to a system of record, because it never sees that call.
The control plane sits above the gateway and answers those questions. Identity, action-level policy, human approval and the audit record are enforced on every action an agent takes, not only on the model requests in between.
AI gateway vs agent framework vs control plane, comparedMoring engineers assemble it inside your environment
AICP provides the reusable control modules. Moring engineers connect them to your identity, cloud, data permissions, security controls and operating tools.
The result is one governed path built around the systems and approval processes you already use.
See how Moring engineers build and deploy workflow agentsSee what is live, what needs approval and what every agent did
m.alvarez@kestrelgroup.example
Agents
Every AI agent running on the platform, bound to a use case, drawing a model, tools and the project's token meter.
| AGENT | BOUND TO | STATUS | SESSIONS · 24H | SUCCESS | TOKEN METER | COST · 24H | ACTIVITY |
|---|---|---|---|---|---|---|---|
| claims-triage-agentsupervised ◯ claude-opus | Claims triage MIMeridian Insurance | Running | 118 | 98.6% | 1.42M · 62% | $21.40 | |
| incident-triage-agentsupervised ◯ claude-sonnet | Incident triage NBNorthwind Bank | Running | 96 | 97.9% | 1.08M · 48% | $14.10 | |
| reconciliation-agentsupervised ◯ claude-sonnet | Statement reconciliation CFCalder Financial | Running | 74 | 99.1% | 820K · 34% | $9.80 | |
| doc-extract-agentsupervised ◯ claude-sonnet | Policy document extraction MIMeridian Insurance | Running | 88 | 98.2% | 940K · 41% | $11.60 | |
| underwriting-research-agentsupervised ◯ claude-opus | Underwriting research MIMeridian Insurance | Running | 36 | 96.4% | 410K · 19% | $6.20 |
m.alvarez@kestrelgroup.example
Approval Queue
Every request awaiting a decision, use cases, model requests, agents, MCP servers and skills, with the stage each is in. Decisions are made by org admins; onboarding by the owning-team lead.
m.alvarez@kestrelgroup.example
claims-triage-agent
RunningSUPERVISED AUTHORITYPOL-CLM-04- Read claim, policy and coverage records
- Assign a triage tier and route the file
- Draft the adjuster summary
- Any payment or reserve change
- Writing a decision to the system of record
- Contacting the policyholder directly
| TIME | ACTION | TOOL | POLICY | RESULT |
|---|---|---|---|---|
| 14:03:20 | Issue advance payment $2,400 | payments | Approval required | Awaiting R. Bennett |
| 14:03:02 | Assign triage tier 2 | claims-core | Allowed | Completed |
| 14:02:44 | Extract loss details from FNOL | doc-ocr | Allowed | Completed |
| 14:01:07 | Query claimant address history | policy-admin | Blocked | Out of scope |
Agents in production
See each agent, its owner, operating status, assigned model and level of authority.
The same control path follows every agent into a real workflow
Banking
Govern what evidence an agent may gather, when compliance must review and what can be written to a system of record.
Explore banking agentsInsurance
Govern how claims, underwriting and servicing agents use policy data, prepare decisions and involve licensed people.
Explore insurance agentsAI Ops
Govern how incident agents investigate, recommend, execute approved actions and verify the result.
Explore AI Ops agentsIn-depth guides for architecting an AI control plane
What is an AI Control Plane and How to Evaluate One
A comprehensive technical checklist comparing control plane capabilities with API gateways and frameworks.
Read evaluation guide →Build vs. Buy an Enterprise AI Control Plane
Engineering economics, maintenance realities, and the true architectural cost of custom internal tool control layers.
Read strategy guide →Enterprise AI Architecture: Where the Control Plane Fits
How the control plane interfaces with existing API gateways, LLM registries, enterprise IAM, and event streams.
Read architecture guide →AI Gateway vs. Framework vs. Control Plane
Clarifying enterprise boundaries: what gateways do, what frameworks build, and what control planes govern.
Read comparison →What Makes an AI Agent Production-Grade?
Five hard architectural criteria that separate prototype scripts from reliable, governed enterprise agents.
Read criteria →Deploying AI Agents in Your Own Cloud Environment
Keeping models, data, and agent execution inside your approved VPC, AWS, or Azure tenant.
Read deployment guide →Taking MCP to Production: Tool Design, Identity, Gateways and Governance
Workflow-shaped tools, context budgets, one identity per server and the gateway that sits in front of them.
Read the MCP guide →Agentic Process Automation vs RPA
Scripts follow steps; agents choose them. That difference is exactly why an agent needs the control plane an RPA bot never did.
Compare agents and RPA →
Scale AI agents without rebuilding the controls each time
The Moring AI Control Plane connects to your existing identity, security and data systems, creating a repeatable path for agents to reach production.