AI AGENT MANAGEMENT · ALL FOUR LAYERS
Manage every AI agent like production software.
Agents on Azure AI Foundry, AWS Bedrock, Google Vertex AI, Salesforce and ServiceNow — and the agents you build yourself — registered, observed, governed at the gateway and, when autonomy demands it, contained in a sandbox with a kill switch.
An AI agent management platform gives the enterprise one place to inventory, monitor, govern and control autonomous AI agents — regardless of which platform each agent runs on. Agents differ from chatbots: they call tools, write to systems of record and act. Managing them takes more than a dashboard. It takes a registry, a policy point and, for the highest-autonomy agents, runtime containment.
Kosmoy is that platform — hyperscaler-independent, deployed in your own Kubernetes, in production at regulated institutions including Banca d’Italia and Leonardo. Agent management in Kosmoy spans all four layers of the platform: register, observe, govern, contain.
Internal and external agents — two depths of control.
For agents you run inside an Action Capsule, you get the full set — inventory, observability, guardrails, routing, kill switch. For agents on someone else’s platform you get inventory, risk classification and use-case matching, plus observability where the upstream platform exposes telemetry. Same dossier. Two depths of control.
The agent lifecycle, as registry states.
Management means an agent is always in a known state, with a named owner and an audit trail for every transition. Discovery feeds the front of the pipeline — connectors and reconciliation surface agents the registry has never seen — and retirement closes it: credentials revoked, record archived, evidence kept.
- Discover
- Assign owner
- Classify
- Approve
- Deploy
- Monitor
- Change
- Suspend
- Retire
Zero-trust identity for agents.
Every agent carries an identity and a named owner in its record. Access is least-privilege and enforced at the gateway; capsule-run agents receive just-in-time credentials that exist only for the run, and sensitive actions wait for pre-flight authorisation. Every tool and system access lands on the audit trail — the same trail compliance reads.
Cost and quality per agent.
Token spend, latency, failure rate and feedback attribute to each agent, with traces showing the steps and tools behind every run. Budgets cap what an agent may spend; evaluations score what it produces. An agent that costs more than the task it completes shows up in the numbers.
What an AI agent management platform covers.
One registry across platforms
Internal Capsule agents and external agents on Azure AI Foundry, AWS Bedrock, Google Vertex AI, Salesforce and ServiceNow — one list, one record shape.
Cost and behaviour per agent
Spend, tokens, latency and feedback attributed to each agent. A budget at the gateway warns as the limit nears and can stop a runaway agent.
Policy at the gateway
RBAC, guardrails and budgets applied to every agent call that routes through Kosmoy — configured once, enforced everywhere.
A2A traffic governance
Agent-to-agent calls carry the same policy as LLM and MCP traffic. Identity travels across agent hops that pass through Kosmoy.
Sandbox for what acts
Action Capsules wrap agents in a kernel-enforced sandbox in your own Kubernetes. The only way out is the capsule's gateway.
Kill switch and evidence
Mission Control supervises the fleet: pre-flight authorisation, just-in-time credentials, kill switch. Every action lands on the audit trail.
The agent management RFP: 13 questions.
Ask every vendor the same thirteen. The pattern to watch for: platforms that answer the visibility questions and go quiet on enforcement and containment.
- Can you inventory agents built on platforms you don't host — Microsoft, AWS, Google, Salesforce, ServiceNow?
- How are discovered agents matched to approved use cases — and what happens to the ones that don't match?
- What fields does the agent record carry: owner, platform, model, tools, data classes, risk tier, lifecycle state?
- Which identity does an agent run under, and can access be scoped to least privilege per task?
- Are agent credentials long-lived secrets, or issued short-lived for each run?
- Which agent actions require human approval before they execute, and where is that enforced?
- Can one policy — guardrails, budgets, RBAC — apply to agent traffic regardless of where the agent was built?
- What do agent traces show: steps, tool calls, retries, failures, cost per run?
- Can you cap an agent's spend, and does the cap block calls or only report them?
- Is there a runtime boundary a compromised agent cannot escape — and a kill switch that ends the run?
- What audit evidence exists per agent for EU AI Act, ISO/IEC 42001 or NIST AI RMF reviews?
- Does the control plane run in our infrastructure, or in the vendor's cloud?
- What happens when an agent is retired — its credentials, its record, its evidence trail?
Module questions, answered straight.
What is an AI agent management platform?
An AI agent management platform gives the enterprise one place to inventory, monitor, govern and control autonomous AI agents — regardless of which platform each agent runs on. It covers four functions: a registry of every agent, observability over cost and behaviour, policy enforcement on agent traffic, and runtime containment for agents that act on systems of record.
Can Kosmoy manage agents built on Azure AI Foundry, AWS Bedrock, Salesforce or ServiceNow?
Yes. The Agents Master Registry connectors pull each platform's agents into one inventory with risk classification and use-case matching. Monitoring applies where the platform exposes telemetry; gateway policy applies to traffic that routes through Kosmoy; full runtime control applies to agents running in Kosmoy Action Capsules.
Do we have to rebuild agents to manage them with Kosmoy?
No. External agents are harvested and registered as they are. Apps and agents that call models switch a base URL — the gateway speaks an OpenAI-compatible API — or use the platform's Bring-Your-Own-Model setting. An Action Capsule is only added when an agent needs containment, not as a precondition for management.
What does the agent kill switch do?
Mission Control can stop any Action Capsule mid-run. The execution lease expires, just-in-time credentials are revoked, and the agent's egress — which only ever went through its gateway — closes. The stop, like every other action, is recorded as an event with timestamp, actor and outcome.
How is AI agent management different from agent building?
Builders create agents — Kosmoy includes a no-code Agent Builder for that. Management covers every agent wherever it was built, including the ones your business units already shipped on other platforms. The platform that builds an agent only sees its own; the management layer sits above all of them.
What is the difference between an AI agent registry and an AI agent management platform?
The registry is the record layer: every agent, its owner, platform, model, tools, risk tier and lifecycle state. A management platform is the registry plus what acts on it — observability over cost and behaviour, policy enforcement at the gateway, and runtime containment. If you can list your agents but not stop one, you have a registry, not management.
How do enterprises apply zero-trust access to AI agents?
Treat every agent as an untrusted workload: give it its own identity and owner, scope access to least privilege for the task at hand, issue credentials just-in-time instead of long-lived secrets, route its traffic through a policy gateway, and log every tool and system access. In Kosmoy, capsule-run agents take this to the runtime: egress physically limited to the paired gateway, credentials that exist only for the run.
Can an agent-management platform stop a runaway agent?
It should — that is the difference between watching and managing. In Kosmoy, two levers apply: a budget cap at the gateway blocks a looping agent's calls once its allowance is spent, and for agents running in an Action Capsule, Mission Control's kill switch ends the run itself, revoking credentials and closing egress.
What should an enterprise include in an AI agent management RFP?
Test four jobs, not features: can the vendor inventory agents it did not build, observe cost and behaviour per agent, enforce one policy across platforms, and contain or stop an agent at runtime? The 13-question checklist on this page covers those plus identity, evidence and deployment boundary — the questions that separate a dashboard from a management platform.
Enterprise proof
Built for environments where AI cannot become another unmanaged SaaS silo.
Kosmoy works with regulated and operationally critical organizations, including Banca d’Italia and Leonardo.
- Runs in your Kubernetes
- Azure · AWS · GCP · on-prem
- LLM · MCP · A2A
- No mandatory vendor-hosted control plane
See agent management end to end.
From harvest to kill switch: registry, traces, gateway policy and containment in one session.
Or email sales@kosmoy.com.