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Palantir recently published the first blog in a series on their Agentic Runtime, a deep dive into how AIP secures agents across the full operational lifecycle. It's a significant piece of content, and not just because of who wrote it.
It's significant because it validates what we've been building at Agno: agents are no longer experiments. They're infrastructure.
And infrastructure needs more than a framework. It needs a runtime, a control plane, security architecture, and observability built in from day one.
We read Palantir's blog carefully. Here's what stood out, and where we see the market heading.
What Palantir gets right about production AI agents
Palantir has been operating in mission-critical environments longer than most AI companies have existed. When they publish a perspective on production AI, it's worth paying attention to.
"Commodity cognition" is the right frame
Palantir notes that frontier models are becoming “increasingly similar in aptitude and performance on key benchmarks.” They call this phenomenon commodity cognition.
This is exactly right. The model layer is commoditizing. GPT-4, Claude, Gemini: they're converging. Differentiation no longer comes from which model you choose, but from the infrastructure that makes that model useful.
This is why we built AgentOS to be model-agnostic from day one. The value isn’t in picking winners at the model layer; it’s in building a runtime that lets you swap models without rebuilding your architecture.
The agent definition that should become standard
Palantir defines an agent as:
"A stateful control loop that repeatedly invokes a stateless reasoning core, interprets its outputs, executes tools and memory options, and feeds the results back until a termination condition is met."
This is a solid definition. It’s technical and precise, and it cuts through a lot of the confusion. An agent isn’t a chatbot or a prompt chain. It’s a control loop with memory, tools, and terminal conditions.
The industry needs more of this kind of clarity. Too much of the “agent” conversation is still stuck in demo land. Palantir’s framing helps push things toward what actually matters: production systems with real operational constraints.
Observability as non-negotiable
"There is no reliability without observability, especially with non-deterministic agents."
We agree. When an agent is operating in a huge decision space with countless possible paths, you have to be able to see what it did, why it did it, and what information it was using at each step.
This isn’t a nice-to-have. It’s the baseline for running agents in production.
Where we agree: The five dimensions
Palantir outlines five security dimensions for production agents:
- Secure and resilient access to the reasoning core. Model access with governance, no retention by providers, unified resource management.
- Insulated orchestration of agent executors. Hardened compute substrate, permission schemes, resilient failover.
- Granular policy enforcement across all forms of memory. Working, episodic, semantic, and procedural memory with consistent security.
- Governed access and utilization of multimodal tools. Tool invocations are dependent on authorization, egress policies, and provenance-based security.
- Real-time observability and post-hoc auditing. Full tracing, version history, and compliance monitoring.
This is the spec. Any serious production agent platform needs to address all five.
At Agno, we built AgentOS against the same requirements:
- Model access: Model-agnostic architecture supporting OpenAI, Anthropic, Google, AWS Bedrock, Azure, and local models
- Orchestration: Stateless, horizontally scalable runtime with request-level isolation
- Memory governance: User profiles, session memories, and knowledge bases with JWT-based RBAC
- Tool governance: Guardrails, human-in-the-loop confirmations, and controlled action execution
- Observability: Tracing built-in, stored in your database, with full session monitoring and metrics
The checklist is the same. The architecture is different.
Where we differentiate: A question of architecture
This isn't about better or worse. It's about what fits your constraints.
Vendor-Managed vs. Self-Operated
Palantir can deploy in your cloud environment, whether thats AWS, Azure, GCP, or OCI. But it isn’t self-deployable. Palantir engineers handle the deployment and management, and their control plane, Apollo, maintains an ongoing connection for updates, monitoring, and orchestration. This is what they describe as a shared security model, where Palantir takes responsibility for infrastructure security.
For many organizations, this is exactly what they want. Palantir's operational expertise is part of the value.
Agno offers a different model: a truly self-operated one.
You deploy AgentOS. You run it. There's no ongoing connection back to Agno, no management plane we control, and no vendor access to your environment. The self-hosted Control Plane option means everything stays under your control: usage, logs, metrics, traces, memory, knowledge, and sessions.
For organizations where vendor access is a concern, whether due to regulation, security policy, or contractual requirements, self-operated architecture eliminates that variable entirely.
Integrated Ecosystem vs. Open Architecture
Palantir’s platform is proprietary, which lets them tightly integrate and govern everything across their stack.
AgentOS is built on an open-source foundation with 37,000+ GitHub stars. The agent framework is Apache-2.0 licensed. You can inspect the code, contribute to it, and deploy it without permission.
For teams that need to audit their dependencies, avoid vendor lock-in, or integrate agents into existing Python infrastructure, open source provides flexibility that proprietary platforms can't match.
Ecosystem integration vs. stack flexibility
Palantir's agentic runtime is part of a larger ecosystem: Foundry for data, Apollo for deployment, and Ontology for governance. The integration is seamless if you're already in that ecosystem.
AgentOS takes a lighter-weight approach. It’s FastAPI-native and drops directly into any Python stack. You can add middleware, custom routes, and background jobs. Integrate with your existing infrastructure without adopting a new platform.
For organizations with established infrastructure investments, this means adding agentic capabilities without re-platforming everything.
White glove support, your way
Both approaches offer enterprise support. Palantir has professional services and bootcamps.
Agno offers dedicated technical leads, custom SSO and RBAC, support SLAs, and self-hosted control plane options. All while your data stays in your environment.
The support is white glove. The architecture has zero egress.
AI agents have entered production
A few years ago, "agents" meant academic research or science fiction. A year ago, it meant demos and prototypes.
Today, Palantir is publishing detailed technical content about securing agents in production. They're talking about memory taxonomies, provenance-based security, and distributed tracing across decision manifolds.
This is the market validation moment.
The question is no longer whether agents will run in production. It's how, and with what architecture.
At Agno, we believe the future includes both approaches:
- Organizations seeking a fully managed, integrated ecosystem with unified governance and Palantir’s operational expertise will find it in AIP.
- Organizations that need self-operated architecture, open-source foundations, and zero vendor access will find it in AgentOS.
The important thing is that the agentic runtime layer is now recognized as essential infrastructure. The "framework wars" were a warmup. The real work is in production systems that are secure, observable, and built for mission-critical operations.
We're glad to see Palantir helping to define the category. And we're excited to offer a different path to the same destination.
How to evaluate an AI agent platform for production use
If you're evaluating agentic platforms for production deployment, here's what we'd suggest:
- Define your constraints first. Vendor access policies? Existing infrastructure investments? Self-operation requirements? Compliance frameworks? These will shape which architecture fits.
- Look at the five dimensions. Any platform you consider should address model access, orchestration, memory governance, tool governance, and observability. Ask how.
- Try before you buy. AgentOS has a free tier. The framework is open source. You can evaluate the architecture before committing.
The agentic runtime has arrived. The question is which one fits your organization.
Ready to explore AgentOS? Start for free or book a demo to discuss enterprise requirements.