AI agents are smart. Maybe too smart.

They can query databases, pull documents, and summarize sensitive information in seconds. But that’s exactly the problem: if your agent isn’t aware of trust and context, it can access the wrong data without anyone noticing.

At IndyKite, we’ve seen this happen in real-world RAG pipelines. The agent doesn’t make a mistake-it tries to accomplish its goal blindly because it doesn’t know what it’s allowed to touch.

The risk: Autonomous agents and data

AI agents operate across multiple systems:

• Vector databases
• APIs
• Internal knowledge graphs
  
  

Without proper checks, they can:

• Read sensitive customer data meant only for support agents
• Pull confidential internal docs into prompts
• Expose regulatory or PII information
  
  

It’s not a bug. It’s a missing trust layer.

Preventing wrong access with context

Instead of letting the agent retrieve anything it can see, we enforce Knowledge-Based Access Control (KBAC) at runtime, through our MCP server for example:

# Agent tries to retrieve data
agent_id = "support-bot:42"
requested_data =indykite.mcp.search("Show tickets for user123")‍

# Only safe, permitted data reaches the agent

‍

Here, indykite.mcp. vector_db.search() triggers Indykite’s KBAC engine automatically, and performs an authorized semantic search on the graph data. In particular, this search automatically checks::

• Subject identity: Who is making the request?
• Purpose/context: Why are they asking?
• Trust score: Is this resource safe for them?
• Filter out from the result set all resources the Actor doesn’t actually have access to.
  
  

This ensures the AI agent can never accidentally access or expose data it shouldn’t.

Another example, using an external Vector database:

# Agent tries to retrieve data

agent_id = "support-bot:42"

requested_data =vector_db.search("Show tickets for user123")

‍

# Context-aware filter

trusted_results = [

   requested_data.forEach {

‍

# Use AuthZEN to evaluate retrieved data, either one by one or through batching

using AuthZEN /evaluations endpoint

  if indykite.authzen.evaluate(subject=agent_id, resource=r, action = read, context="support")

trusted_results.add(requested_data($current)

   }

]

# Only safe, permitted data reaches the agent

‍

Why this matters for developers

Every AI system you build has autonomous actors. Treating them like “just another user” is dangerous.

By baking in trust at the data layer, you:

• Prevent accidental data leaks
• Maintain compliance with regulations
• Make AI systems explainable and auditable, because you can rely on deterministic guardrails.
  

Join the builders who care about AI agent trust

Your AI agents can do incredible things but only if they respect boundaries.
At IndyKite, we give developers tools like Knowledge Based Access Control to enforce those boundaries at runtime, so trust is always computed, not assumed.

👉 Explore IndyKite’s sandbox
👉 Try KBAC and AuthZen examples
👉 Share your agent boundary stories

Smart AI without trust is just a liability.