The rise of the agent runtime: The compute platform behind production agents

The fast pace of AI research means organizations now have a wide range of models to choose from that can power AI agents to solve real business problems. But choosing a model doesn’t guarantee you effective agents or even good performance. For that, you need to run your agents in an environment that provides them with tools, state, security, and scale, with fast startup times and good integration with your existing business systems.

Picking the right agent runtime environment is like picking an enterprise app server but for AI systems — and agents have very different needs from traditional applications.

Gartner predicts more than 40% of agentic projects will be canceled by 2027; not because models aren’t powerful enough to be useful but because of unclear business value, inadequate risk controls and ballooning costs.

Gartner predicts more than 40% of agentic projects will be canceled by 2027; not because models aren’t powerful enough to be useful but because of unclear business value, inadequate risk controls and ballooning costs. Agents that deliver in proof-of-concept systems will fail in production if the runtime stack powering them can’t keep up and keep them under control.

Agentic compute is different

It’s easy to think of an AI agent as just another microservice that takes unstructured input, runs APIs or queries, and returns messages. But infrastructure designed for traditional enterprise applications with predictable business logic or even cloud-native stateless workloads doesn’t fit agents with their bursty, long-running, stateful, non-deterministic, code-writing, tool-invoking behaviors that might be triggered by a system event or an email — not just a chat session.

Model inference needs GPUs for speed, but agents also need reliable, durable compute that supports stateful sessions for long-running processes, along with strong security and real-time visibility.

You still need to think about familiar issues like hosting, scaling, identity, and securi…