Shared agentsGamma Platform
Secure and control every agent, API, and event.
- AI & Platform Governance
//Agent Management
Secure and controlyour AI agents.
Unified security and governance for all your LLM, MCP, and agent-to-agent (A2A) traffic.
Audit
Full audit trail
Every LLM, MCP, and agent-to-agent interaction, in one navigable trace.
Cost
Cut LLM costs
Token limits, semantic cache, and real-time usage controls.
Security
Secure every AI interaction
Fine-grained access control on every call.
AI agents are already embedded in production systems.
AI adoption is outpacing enterprise control.
//Overview
What is AI agent management?
AI agent management is the security and governance of AI agents and their traffic: what models they call, which tools they use through MCP, how they talk to other agents through A2A, and who is accountable for each action.
DataObserve Sense manages agents through a catalog that is the single source of truth, LLM, MCP and A2A proxies that enforce policy on each kind of traffic, agent identities for every actor and lineage for every action.
//How it works
From scattered agents togoverned workflows.
Step 1
A single source of truth
One registry for models, MCP servers, tools, skills, prompts, and agents, with APIs and Kafka streams turned into tools.
//Manage agents with confidence
Agent-first, without losing control
Enterprise-grade security
Strict access controls and policies on every interaction.
Centralized control
Every agent and tool under one control plane.
End-to-end visibility
How agents operate, interact, and decide.
Catalog-aware authorization
Access control that references your real tools, models, and agents.
Agent identity for every actor
Desktop agents, hosted workflows, and workloads each get the right credential.
Unified ecosystem
Agents, models, and systems in a framework that fits your architecture.
//Explore more withDataObserve
More from Sense · AI & Platform Governance
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Full lifecycle API management across REST, GraphQL, and gRPC, with federated governance over the gateways you already run.
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Event Management
Make your Kafka event streams agent-ready, with the security, governance, and access control your organization requires.
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Authorization Management
Zero-trust security across all AI, API, and event traffic, with catalog-aware policies decided inside every gateway.
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//Use cases
Agent Management: use cases
LLM cost control
Route, cache and limit model calls through one front door.
Tool governance
Decide which agents may call which MCP tools, with what data.
Multi-agent systems
Govern agent-to-agent (A2A) calls the same way as API calls.
Audit
Keep a full trail of every agent action for security and compliance reviews.
//FAQs
Agent Management: questions teams ask
What is MCP?
The Model Context Protocol (MCP) is an open standard that lets AI agents discover and call tools and data sources. Governing MCP traffic controls what agents can do.
What is A2A?
Agent-to-agent (A2A) is a protocol for AI agents to communicate and delegate work to each other. An A2A proxy applies policy and logging to those calls.
Does agent management work with any model provider?
Yes. The LLM proxy sits in front of the model providers you use, so policy and cost controls apply across all of them.
What is an LLM proxy?
An LLM proxy is a single endpoint in front of language model providers that authenticates, routes, limits and logs every model call.
