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Agent systems have always needed more than frameworks and runtimes. Our latest release delivers the missing production layer and completes the stack.
There’s a gap in agentic AI that no amount of model intelligence will close.
Teams can build agents. They can make them fast. But the moment you ask—Who approved that action? How do we audit this at scale? Can I explain this system to a compliance officer?—agent infrastructure goes quiet.
That gap isn’t new. Every wave of infrastructure software hits the same wall. Layer 1, libraries and frameworks, help you build. Layer 2, runtimes, help you execute. But neither gets you to production, not the kind of production where organizations stake real business processes on the system. For the stack to be complete, you need a third layer.
Our goal has always been to deliver the full stack. The framework was layer one. AgentOS was layer two. And last week, with v2.5, we delivered layer three and completed the picture.
Layer One: Framework
Frameworks solved the cold-start problem, and that was huge. Agno’s framework gives you abstractions for tool use, memory, retrieval, and multi-step reasoning so you're not hand-rolling every agent from raw API calls. That's valuable. But by itself, it answers one question: how do you build an agent?
It doesn't address who approved a given action, how six agents share a knowledge base without contaminating each other's results, or how you enforce a policy that requires human sign-off before anything touches the payments API. Those are different questions that frameworks, by definition, don’t answer. Answering them requires another layer.
Layer Two: Runtime
AgentOS answered the next critical question: how do agents execute at speed and scale?
A high-performance runtime purpose-built for multi-agent systems, Agno’s AgentOS handles execution, session management, storage, and the core infrastructure that makes agents run fast and reliably. It's the reason teams choose Agno over stitching together their own execution stack.
That layer, too, is still there and still essential. But even a great runtime leaves a set of questions unanswered. How do you govern what agents are allowed to do? How does the system get smarter over time instead of starting from zero every session? What happens when the nightly pipeline fails at 3 a.m. in a timezone nobody configured? How do you swap orchestration patterns without rewriting agent logic?
These aren't framework problems. They aren't runtime problems. They're production problems. And they need a third layer.
Layer Three: Production
Our latest release doesn't announce a platform or an operating system. It does something more subtle and, I think, more important: it fills in the production layer, the connective tissue between "I built an agent" and "we run agents as a business capability."
It’s the third layer. It doesn't replace the framework or the AgentOS, it completes them.
Look at what shipped, not as a feature list, but as a set of bets about what production agent systems actually need.
Orchestration as a first-class choice, not a custom build. Four team execution modes, coordinate, route, broadcast, tasks, aren't just convenience methods. They're a recognition that orchestration topology is a design decision, not an implementation detail. You should be able to name your pattern, swap it, and audit it. The moment orchestration lives in bespoke glue code, you've lost governability.
Human oversight as architecture, not afterthought. The approval system and expanded HITL controls aren't bolted-on safety theater. Blocking gates, audit-mode logging, tool confirmation mid-run, external execution handoffs, these are the primitives you need to deploy agents in any environment where "move fast and break things" is not an option (which is most environments).
Time-aware execution without external dependencies. Built-in cron scheduling with retries, timeouts, and timezone support sounds mundane until you realize it eliminates an entire category of infrastructure, Airflow DAGs, standalone cron servers, Lambda triggers, that teams currently duct-tape around their agent systems. The best infrastructure is the kind you stop thinking about.
Shared resources with isolated behavior. The isolate_vector_search feature is a small line item with large implications. Sharing a vector database across agents while guaranteeing isolated retrieval means you can scale your knowledge infrastructure without scaling your operational complexity. This is a multi-tenancy primitive, and its presence signals that Agno is thinking about fleet-scale deployments, not single-agent experiments.
Accumulated intelligence across sessions. LearningMachine for teams means agent systems get smarter over time without manual re-prompting. This is the difference between a tool and a colleague. Tools do what you tell them, every time, from scratch. Colleagues remember what worked, what didn't, and what the constraints are.
Runtime-resolved configuration. Callable factories for tools, knowledge, and team members mean the system adapts to context at execution time, not at deploy time. Different user? Different tenant? Different environment? The agent configures itself accordingly. This is how you build agent infrastructure that serves a platform, not just a single application.
The Full Agentic Stack
Step back, and a coherent philosophy emerges. Agno isn't trying to be the thing that builds your agents. It's trying to be the thing that makes your agents operationally real, governable, schedulable, auditable, multi-tenant, and capable of learning. It's the layer that sits between your agent logic and your production environment and handles everything that has nothing to do with intelligence but everything to do with trust.
The industry has been obsessed with making agents smarter, and that work matters. We've invested heavily in it ourselves. But intelligence alone was never the bottleneck. The bottleneck is making intelligent systems trustworthy enough to deploy. Trust requires approval gates, audit trails, isolated data access, and predictable orchestration you can explain to a compliance officer. It requires the system to get better over time so you're not re-earning trust from scratch every session. Intelligence gets you the demo. Trust gets you the deployment. That's what’s been missing. And that’s what this layer delivers.
For the first time, the agentic stack is complete. The framework gives you the abstractions to build agents. AgentOS gives you the runtime to execute them at speed. And the production layer gives you everything required to deploy them with trust.
Each layer is necessary. None is sufficient on its own. This is the release that brings them together.