# Evolution of agentic surfaces — Gagan Bhat & Isabella Kai He, Anthropic

## Executive summary

The evolution of agentic surfaces (from the Messages API to Claude Managed Agents) addresses the challenge that static harnesses become limiting factors as underlying LLMs improve. The core architectural shift is decoupling the agent's 'brain' (the reasoning loop) from its 'hands' (the tool execution environment/sandbox). This separation significantly improves reliability, allows for parallel setup, and boosts performance by achieving up to 60% faster time-to-first-token at P50. Managed Agents provide production-grade infrastructure—including session logging, credential vaults, and self-hosted sandboxes—enabling developers to focus on defining the agent's task and context rather than managing complex operational overhead.

## Key takeaways

- Harnesses Encode Stale Assumptions: As models improve (e.g., Opus 4.5 eliminating 'context anxiety'), fixes built into older harnesses become pure overhead, adding latency or discarding cache incorrectly. Managed Agents are designed to be agile and adapt to model evolution.
- Decoupling Brain from Hands: Separating the agent loop (brain) from the tool execution environment (hands/sandbox) improves reliability, allowing the brain to resume from a durable session log even if the hands fail. It also allows model reasoning to start immediately while container setup runs in parallel.
- Self-Improving Agents via Dreaming: The system utilizes durable session logs and memory state, which can be fed into a periodic batch process called 'dreaming.' This process extracts new insights to automatically update the agent's memory, making subsequent sessions smarter.

## Technical details

- Agentic Surface Evolution: The progression moved from simple token-in/token-out (Messages API) to manual, complex agent loops (requiring custom handling of session management, credentials, and sandboxing), culminating in the Claude Agent SDK, and finally to Managed Agents which provide a complete, production-grade stack.
- Managed Agent Architecture: The system is built around three core primitives: the **Agent** (defining model, prompts, tools), the **Environment** (the isolated container where the agent runs), and the **Session** (a durable resource persisting all interactions). This architecture enables robust features like state management (Idle, Running, Rescheduling, Terminated) and context recovery.
- Context Engineering & Resilience: Managed Agents solve 'context rot' by logging every interaction to a durable session log. If the model discards context mid-run, the harness can read specific context slices back from this persistent log resource.
- Advanced Capabilities: Outcomes: Outcomes allow users to define explicit success criteria (a rubric) for an agent. A separate 'grader agent' runs alongside the main loop, continuously checking if the task meets the defined success criteria and retrying until completion.

## Practical implications

- Engineers can build production-scale agents without managing complex infrastructure components like credential handling or session state.
- The decoupling of brain and hands allows for significantly improved latency (up to 90%+ improvement at P95) by running setup in parallel with reasoning.
- Security is enhanced through the use of secure vaults, ensuring that sensitive security tokens are never exposed directly to the model.
- Teams can run tool execution environments within their own Virtual Private Cloud using self-hosted sandboxes for maximum control.

## Topics

Agentic Systems, LLM Orchestration, Context Management, Distributed Computing, Production Infrastructure, Messages API, Claude Agent SDK, Claude Managed Agents

Source: https://www.youtube.com/watch?v=K0X9QDRkIdg