Why This Matters
Agents do not run forever. Context windows fill up. API sessions time out. Users close their laptops. The server restarts. When any of these happen, all the agent's accumulated context — what it was working on, what it tried, what failed, what it decided — vanishes.
Handoff solves this. Before the agent stops, it serializes its state into a checkpoint: the current task, progress so far, pending decisions, and relevant context. Later, another agent (or the same agent in a new session) loads that checkpoint and continues. The work does not start over. The new agent picks up mid-task with full awareness of what happened before.
This is how production systems handle long-running coding tasks. A refactor that spans 50 files cannot happen in a single context window. The agent works on the first 15 files, checkpoints its progress, and hands off to a fresh agent that tackles the next 15.
What You Will Build
A HandoffManager that creates checkpoints, serializes them to disk, and restores agent state from a checkpoint. It supports both planned handoffs (agent decides to hand off) and emergency handoffs (timeout or context limit forces a stop).
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What's Next
You have completed Part III of OrchestCode. Your system now has the full orchestration layer: a supervisor that delegates to workers (Chapter 9), a scheduler that manages dependencies and parallelism (Chapter 10), a review queue that gates critical changes behind human approval (Chapter 11), and a handoff system that enables seamless context transfer between agents (Chapter 12).
Together, these four chapters transform a single-agent system into a multi-agent orchestration platform. The patterns you have built — task decomposition, DAG scheduling, human-in-the-loop review, and stateful handoff — are the same patterns used by production systems like OpenAI Codex, Devin, and Factory.
From here, the system is ready for production hardening: observability, scaling, security, and deployment. Each orchestration component you built has clear interfaces and can be independently scaled and monitored.