Deep Agents

Deep agents are AI systems engineered to execute extended, multi-step workflows autonomously. Over the course of hours or more, they maintain persistent memory, plan and re-plan strategies, decompose objectives into sub-tasks, spawn dedicated sub-agents, and coordinate via a shared artifact workspace. They checkpoint progress, detect and recover from failures, and can pause or resume execution as needed — all while iterating until the end goal is reached.

Key ideas

• Planning tools to break down complex tasks into steps and subgoals.
• Sub-agents with specialized context and quarantined memory.
• Persistent state beyond chat history, often via a virtual file system.
• Detailed system prompts to steer behavior and tool use.

Example workflow (illustrative):

Orchestrator agent receives “Generate a market intelligence report on X industry.”

It plans subtasks: (a) collect data sources, (b) run competitor analysis, (c) synthesize, (d) draft report, (e) fact-check.

It spawns sub-agent A for data collection with web / API tools, sub-agent B for competitor analysis (running models), sub-agent C for writing, and sub-agent D for verification.

All agents share a file workspace: data files, interim tables, notes.

Periodically, the orchestrator writes checkpoints. If sub-agent B fails midway, it resumes from its last checkpoint.

If new constraints emerge (e.g. budget limit), it revises the plan on the fly.

When done, it assembles final report, audits for consistency, returns to user with reasoning log.

How Distri helps

• Rust runtime with predictable performance for long-running agent loops.
• Streaming events and durable execution for observability and resilience.
• Immediate HTTP/SSE APIs and A2A compatibility for easy product integration.