The main line of Agent development in 2026: moving from Agent Framework to Agent Runtime.

The most obvious change in 2026 is that the Agent technology stack begins to be layered.

In the past, many teams packed prompts, tool calling, memory, workflow, permissions, logs, and deployment into one application.

The industry is now being split into several layers: 1 User entrance layer: ChatGPT / Codex App / VS Code / GitHub / Enterprise portal ↓ 2 Harness layer: prompt, tools, memory, subagent, planner, skills ↓ 3 Runtime layer: session, resume, event log, checkpoint, HITL, trace, fork ↓ 4 Compute Layers: sandbox, devbox, container, remote VM, K8s actor ↓ 5 Protocol layer: MCP, A2A, Agent Skills, AGENTS.md ↓ 6 Governance layer: identity, policy, audit, eval, observability, cost This Stratification is not a pure theory.

When OpenAI updated the Agents SDK in April 2026, it focused on model-native harness, native sandbox execution, and the separation of harness and compute; it also made it clear that agents need to be able to check files, run commands, edit code, and process long-horizon tasks in a controlled sandbox.

LangChain also made the problem very straightforward in the Deep Agents runtime article in April 2026: A good harness is responsible for prompts, tools, and skills, but a production-level long-task agent also needs durable execution, memory, multi-tenancy, human-in-the-loop, and observability.

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  thesis["The main line of Agent development in 2026: moving from Agent Framework to Agent Runtime. The most obvious..."]
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  implication["LangChain also made the problem very straightforward in the Deep Agents runtime article in April 2026: A go..."]
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