Inside Managed Deep Agents: mda CLI, Context Hub, and Harbor Evals | Interrupt London
Summary
Managed Deep Agents addresses the complexity of moving sophisticated AI agents from local prototypes to reliable, multi-tenant production systems. The platform tightly couples the agent's core logic (the 'harness') with robust infrastructure components (like LangSmith Deployment), allowing development teams to focus purely on business logic. Key features include centralized authentication, durable execution with checkpointing, version-controlled context management via Context Hub, and streamlined deployment using the `mda CLI`.
Key takeaways
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Agent Evolution: From ReAct to Model + Harness
An agent is defined not just by its model, but by the 'harness'—the surrounding system that provides skills, memory, and system prompts. This harness is crucial for providing the model with the correct context at the right time.
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Managed Deep Agents Simplifies Production Deployment
8:53
The platform abstracts away complex infrastructure concerns like multi-tenancy, durable execution, and task queuing, which are typically difficult to manage when building agents from scratch.
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End-to-End Agent Lifecycle Management
10:54
The process is streamlined via the `mda CLI`: `mda init` scaffolds the project, `mda dev` runs it locally in LangSmith Studio, and `mda deploy` handles production deployment.
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Advanced Governance and Context Control
The system supports granular control over identity and context, allowing developers to define whether an agent uses user-scoped or agent-scoped credentials, and to manage personal vs. shared memory based on the calling channel (e.g., Slack DM vs. shared channel).
Technical details
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Agent Harness Components
2s
A robust agent harness must manage four key areas: 1) Execution Environment (with first-class support for file systems and sandboxing); 2) Context Management (progressive disclosure, summarizing, and offloading large tool outputs); 3) Delegation (decomposing tasks to specialized subagents); and 4) Steering (human-in-the-loop approval).
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Authentication and Identity
840s
Managed Deep Agents use a centralized auth handler service to establish the principal for a call. Developers can explicitly define whether the agent should use user-scoped or agent-scoped credentials when interacting with tools or external services.
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Durable Execution and State Management
Leveraging the LangGraph runtime, the system supports durable execution and checkpointing, enabling agents to pause a run and wait indefinitely for user authorization (e.g., in Slack) and resume exactly where they left off.
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Context and Evaluation
Context is managed through LangSmith Context Hub, providing version control and a shared space for business users to collaborate on skills. Evaluation is simplified via first-class integration with Harbor, which can be initialized using `mda evals init` to set up checks and datasets.
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New Capabilities
Recent additions include HTTP channels (allowing custom channel integration), agent-created schedules (allowing agents to autonomously schedule reports or tasks), and per-run configuration for creating dynamic agents.
Mentioned resources
- Managed Deep Agents
- mda CLI
- LangSmith Studio / LangSmith Deployment
- LangSmith Context Hub
- Harbor
- Parallel Search
Channel & topics
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