Intentra
Runtime Observability and Security
for Agentic AI and MCP
Agentic AI systems don't just chat anymore. They take actions: call tools, hit services, chain steps together. We help you see what's really happening.
The Problem
When Agents Act Unexpectedly, You Need Ground Truth
The Reality
- Agents make autonomous decisions in your infrastructure
- Logs are scattered, incomplete, and lack context
- No unified timeline of what actually happened
- Patterns across runs remain invisible
The Questions You Need Answered
- Which tools did the agent invoke and in what order?
- What data did it access or modify?
- What processes did it spawn or connections did it make?
- Which behavioral patterns keep recurring?
Discovery: The Creative Side of Agents
When you observe agents closely, you start to see surprising behavior patterns
Finding a
Way
Agents don't give up when blocked. They find alternative paths, try different tools, and work around constraints in creative ways.
Novel Compositions
Agents compose tools in sequences you didn't design. They discover emergent capabilities through combination and iteration.
Capability Expansion
Capabilities effectively expand through tool combination, even when you didn't intend it. Simple tools become powerful when chained.
Surprising Toolchains
Surface unexpected sequences that keep appearing. Patterns that reveal how agents actually think about problems.
Philosophy
What We Believe
Learning Faster
This isn't about claiming perfect detection. It's about accelerating your understanding of agent behavior through observation.
Spotting Patterns
Behavioral patterns emerge across runs. See what agents consistently do, not just what they did once.
Practical Governance
Explore what comes next: registries, gateways, and policy-controlled tool exposure.
Get Started
Put Your Agents Under the Microscope - See what your AI agents are really doing at runtime.
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