How to Build Reliable Agent Systems

This article explains how to deploy a long-running service-style Agent system after building a strong base Agent with the OneAgent + MCPs paradigm.

The problem is not how to run an Agent locally with a script. The problem is how to serve many users with multi-request concurrency, multi-turn conversations, user profiles, persistent state, capacity planning, and performance tuning.

Scope

The source article is based on my practical work in an Ant Group intelligent-agent application. It discusses Python-based production Agent systems from the perspective of someone who previously worked mainly as a Java engineer.

The goal is not to present a perfect universal framework. It is to expose the engineering questions that appear when an Agent moves from demo to service.

Agent Runtime as Builder, Executor, and Session Pool

The runtime is divided into three responsibilities:

• Builder: creates domain Agents in the service layer. Different domains can choose different construction methods, including simple LLM calls, workflows, multi-Agent systems, or OneAgent + MCPs.
• Executor: wraps a unified run interface and handles access to sessions.
• Session Pool: reuses Agent instances by session and cleans up expired instances.

This separation keeps domain knowledge and infrastructure concerns apart. Business teams can decide how to build their Agents, while the platform provides scheduling, sessions, execution, and reliability.

Why Actor Thinking Helps

Each Agent instance can be viewed as similar to an Actor:

• an Agent system maps to an application loop plus a universal Agent pool;
• an Agent instance maps to a session plus Agent instance;
• a message queue maps to an async event queue;
• a mailbox maps to session state and checkpointing.

This view helps make concurrency and state boundaries clearer.

Reliability Concerns

A production Agent system must answer questions that a notebook demo can ignore:

• How are sessions created, reused, expired, and isolated?
• How do concurrent requests avoid corrupting state?
• How is user profile data injected without polluting the whole context?
• Where are checkpoints stored?
• How are retries, timeouts, cancellation, and failures handled?
• How is runtime capacity estimated?
• Which metrics show latency, cost, and quality degradation?

Core Claim

Reliable Agent systems are not created by a stronger model alone. They require runtime architecture: session management, state persistence, execution isolation, tool boundaries, capacity planning, and observability.

The full Chinese source article includes detailed architecture diagrams, runtime chains, and production implementation notes.