AI Agent Context Graph by Akto
Context Graphs for your AI Agents, MCPs and LLMs.
66 followers
Context Graphs for your AI Agents, MCPs and LLMs.
66 followers
As Agentic AI adoption rises in organizations, most security teams lack visibility into data flows and context of the AI agents built or used by teams. Akto changes that. The Agent Context Graph auto-discovers and maps your entire agentic system - agents, MCP servers, LLMs, databases, and external APIs, in one live view so you can understand data flow and assess security impact at a glance. Get complete context of your AI agents in Akto!
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Hey Product Hunt, Krishanu here from Akto 👋
12 months ago, when we started working with enterprises for their AI Agent security requirements, from day 1, the real gap was clear.
It is visibility, observability and context.
Security teams are trying to answer the same question:
How do we get complete visibility into our agentic systems?
What does this agent actually connect to?
What are the data flows? What’s the context?
Agents call MCP servers. MCP servers hit databases and external APIs. LLMs get invoked across frameworks like LangChain and LlamaIndex. None of it is visible. None of it appears in a traditional asset inventory.
To solve this gap, Today we are launching AI Agent Context Graph.
Akto auto-discovers every agent, MCP server, LLM, RAG database, and AI chatbots in your agentic system and maps the relationships visually. You see the data flows, the dependencies, and gets complete understanding of your AI attack surface.
Who it's for: Security, Governance, and platform teams looking to govern AI agent adoption across their organization. We've been working with enterprises building and using hundreds of agents across teams. Their inputs shaped every part of Context Graph.
If solving AI Agent Governance and Security with Context graph excites you, drop your comment here and let us know.
How does the context graph help identify security gaps that traditional API testing would miss? Congrats on the launch!