From Pi Agent to AIRUN: Building Enterprise-Grade Agent Runtime

I. The Gap Between Agent Demos and Production

1.1 What Separates Demo from Production

Running an Agent locally through a demo is straightforward: receive a user request, call the model, execute a few tools, and return the result. However, enterprise environments demand far more. For example, an Agent that manipulates codebases may need to read/write files, run commands, and access internal systems. Without unified permission control and sandbox isolation, a single erroneous tool call could affect real business systems. Moreover, a task that takes tens of minutes cannot rely solely on in-process memory for context; a process restart or instance switch would interrupt the task, forcing a complete re-execution.

Other challenges include: ensuring session isolation for concurrent users, enabling administrators to trace the full execution flow when tool operations fail, securely storing credentials, and managing different Agent frameworks uniformly. These issues are often bypassed in demos but are unavoidable in production.

These are not model capability problems; they are runtime problems.

1.2 Industry Answer: Managed Agents

As Agents enter the enterprise, the missing piece is not a stronger model but a production-grade runtime environment. This has become an industry consensus: from LangChain's Managed Deep Agents to Anthropic's Claude Managed Agents, vendors are emphasizing the same trend—hosting the Agent Harness together with the infrastructure required for execution.

The Agent Harness defines how the Agent drives the model, calls tools, and maintains sessions; the hosted infrastructure covers sandboxes, state storage, event streams, permission control, authentication, and deployment capabilities. Combined, they form Managed Agents: developers no longer assemble these components themselves; the platform provides them uniformly, enabling Agents to move from "runs locally" to "production-ready."

AIRUN embodies this approach: it does not bind to a single Agent framework but consolidates runtime capabilities as platform services, allowing different execution engines to run on the same enterprise-grade foundation.

II. Division of Responsibilities Between Pi Agent and AIRUN

2.1 Pi Agent Positioning

Pi Agent is a lightweight, modern Agent execution engine. Its core responsibility is driving a single Agent's execution loop: receiving task context, invoking the model, initiating tool execution, maintaining session state, and emitting incremental events during execution. It abstracts "how the model continuously thinks and acts" into reusable execution components, so developers don't have to implement the Agent loop from scratch.

From an engineering boundary perspective, Pi Agent solves "how a single Agent is executed," but multi-tenant governance, platform-level sandbox scheduling, unified auditing, and deployment management are typically provided by an upper-layer platform.

2.2 AIRUN Positioning

AIRUN positions itself as an enterprise-grade Agent runtime platform. Beyond the execution engine, it uniformly provides the infrastructure required for production: session management, state persistence, event streaming, sandbox execution, permission control, secret management, and deployment capabilities.

After integrating with AIRUN, Pi Agent becomes a schedulable execution engine on the platform, sitting at the same level as other engines like DeepAgent. The platform decides which engine handles a session, in what environment it runs, how state is persisted, and how events are emitted externally. This allows the execution engine to stay focused while runtime governance is handled centrally by the platform.

2.3 Integration Principle

The integration principle can be summarized as "engines are replaceable, the platform remains unified." Pi Agent only handles execution logic and does not directly assume AIRUN's permission system, event storage, or sandbox governance. Conversely, AIRUN does not intrude into Pi Agent's internals; instead, it connects through a unified integration boundary to handle session recovery, event transformation, tool routing, and runtime control.

This layering brings value: platform capabilities do not depend on any single Agent framework, so new execution engines can be added later. Meanwhile, the execution processes from different engines are normalized into the same platform event format, so upper-layer applications don't need per-engine adapters. Developers focus on defining the Agent's model, tools, and behavior configuration, while AIRUN ensures safe, reliable, and observable operation in production.

III. How Pi Agent Integrates with AIRUN

3.1 Execution Path

After a user sends a message, the AIRUN platform first determines which execution engine should handle the request based on session information. When the engine type is Pi Agent, the platform connects via WebSocket to the corresponding execution service and initiates the task.

Tool calls generated by Pi Agent during execution do not act directly on the real environment; instead, the platform routes them to a secure sandbox for execution. Events produced during execution—such as tool call status, model output progress, and task completion status—are streamed back to the platform in real time. The platform persists them and forwards them to the frontend or external systems. Throughout this process, both session state and events are durably stored, providing a basis for subsequent auditing and recovery.

3.2 Multi-Engine Design

Pi Agent and DeepAgent are two execution engines integrated at the same level in AIRUN. The platform does not bind capabilities to a specific framework; instead, it uses a unified session, event, and interface layer to shield underlying differences. This means regardless of which engine drives the current session, the upper layer sees the same session semantics, event format, and interface contract. The frontend need not know which engine is executing behind the scenes, and future Agent frameworks can be onboarded in the same way to reuse platform capabilities. The multi-engine design lets AIRUN maintain platform unity while preserving flexibility and extensibility at the execution layer.

IV. Key Production-Grade Capabilities

4.1 Recoverable Session State

Agent tasks often cannot be completed in a single Q&A round. They may require multiple tool calls, waiting for external system responses, or span long execution times. If context is kept only in process memory, a process restart or instance switch interrupts the task, forcing the user to restart the request—wasting model inference costs and degrading experience.

To address this, AIRUN persists Pi Agent's session state to TiDB. Execution context, tool call records, and task progress are saved with the session. When the service reopens the same session, it can resume from the latest state rather than starting from zero. This gives long-running tasks the basic reliability required in production: even if the running instance switches, the task context remains recoverable.

4.2 Safe and Idempotent Tool Execution

A key difference between Agents and ordinary applications is that Agents actively call tools and modify external environments. AIRUN imposes two constraints on tool execution: a security boundary and idempotent recovery.

For security, tool calls are not applied directly to the real environment but are routed by the platform to a sandbox. File reads/writes, command execution, and other operations occur in a controlled environment where the platform can limit accessible resources and permissions, reducing the risk of accidental damage.

For idempotent recovery, tool invocations use stable call identifiers. When the service recovers from an anomaly, the system can identify duplicate calls via these identifiers, preventing the same tool operation from being executed repeatedly—for example, avoiding duplicate file writes or command executions. Thus, the Agent can continue after a failure without introducing new side effects from the recovery mechanism.

4.3 Unified Events and Observability

Production Agents cannot be black boxes. Administrators need to know which tools were executed, what outputs were produced, and at which step a failure occurred. Frontends also need to display execution progress in real time so users understand the current task state.

AIRUN maps the execution processes of different engines into a unified platform event format. Whether the underlying engine is Pi Agent or DeepAgent, model outputs, tool calls, and task statuses are converted into consistent events and sent back to the platform. The platform then pushes events to the frontend in real time and persists them for auditing, troubleshooting, and performance analysis.

Unified events enable AIRUN to maintain consistent observability across a multi-engine architecture: users see a stable execution process, and the platform records a complete, traceable execution trail.

V. Platform Value

5.1 For Developers

For developers, AIRUN's value lies in freeing Agents from "framework selection and runtime maintenance." Developers only need to define the model, tools, context, and business behavior; session management, state persistence, sandbox execution, event callbacks, and audit observability are provided uniformly by the platform. Thus, an Agent does not need to reimplement an entire infrastructure stack just to enter production.

5.2 For Enterprises

For enterprises, AIRUN provides a unified governance boundary. Tool execution is confined to sandboxes, access behavior is subject to permission control, and the execution process generates a complete event stream, facilitating auditing, accountability, and fault diagnosis. Compared to automation tasks scattered across scripts and personal environments, a managed runtime makes Agent usage more controllable and better aligned with enterprise security and compliance requirements.

5.3 For the Platform

For the platform itself, the peer-level integration of Pi Agent and DeepAgent demonstrates that AIRUN's multi-engine architecture is practically viable. Platform capabilities do not depend on a specific framework but are consolidated in the common layers of sessions, events, sandboxes, permissions, and observability. When new Agent engines are added later, these capabilities can be reused, avoiding the need to build a separate runtime environment for each framework.

VI. Conclusion

As model capabilities gradually converge, competition in the Agent space is shifting from "whose model is smarter" to "whose runtime is more reliable." Enterprises need not only the model's understanding and generation abilities but also controllable permissions, safe execution, recoverable tasks, and traceable processes. These capabilities go beyond the model itself and beyond the boundaries of any single Agent framework, requiring a complete runtime system to support them.

From this perspective, AIRUN connects not just a new Agent engine but a path from technical capability to production capability. Pi Agent ensures efficient Agent execution; AIRUN ensures every execution occurs within a safety boundary. When these infrastructures become platform capabilities, AI can transform from sporadic intelligent behavior into reusable, operable, and continuously evolving enterprise productivity.