Agent Orchestration

Multi-Agent Coordination

Decompose complex tasks into specialized agents with clear domains and protocols for collaboration. A single “God Agent” suffers from context dilution and high error rates; specialized agents provide modularity and better accuracy.

Implement a coordinator-worker or peer-to-peer pattern. The coordinator handles high-level planning and task decomposition, while workers execute specific sub-tasks (e.g., “Research Agent,” “Coding Agent,” “Verification Agent”). Define standard JSON schemas for agent-to-agent communication to ensure interoperability and type safety across different models.

Anti-pattern — The Monolithic Agent: Forcing a single model instance to handle research, planning, execution, and self-correction in a single long prompt. This increases the chance of hallucinations and makes it impossible to swap specialized models for specific sub-tasks (e.g., using a cheaper model for research and a stronger one for coding).

Stateful Workflow Persistence

Treat agentic workflows as long-running state machines that can be paused, resumed, and audited. Agents should not rely on ephemeral in-memory state for multi-day tasks.

Store the “Agent State” (history, goals, variables, tool outputs) in a persistent database (PostgreSQL, Redis). This enables resilience against process crashes and allows for long-running “human-in-the-loop” pauses where the agent waits for approval. Use a versioned state schema so that agents can migrate state across updates.

Anti-pattern — Ephemeral Action Loops: Running an agent in a simple while loop without persisting state to disk. If the process dies or the connection drops after 4 hours of work, the agent loses everything and must restart from scratch.

Unified Memory & Context Management

Maintain a shared “blackboard” or memory layer that agents can read from and write to. Context should be managed as a first-class resource, pruned and summarized to avoid context window overflow.

Differentiate between:

1. Short-term Memory: The immediate conversation/action history.
2. Medium-term Memory: A persistent “scratchpad” for the current task.
3. Long-term Memory: Vector-indexed historical knowledge and user preferences.

Use a “Memory Manager” to summarize old context and inject relevant snippets into the agent’s prompt based on the current sub-goal.

Human-in-the-loop (HITL) Orchestration

Design for “meaningful human control” by inserting checkpoints for high-risk actions. Automation is most effective when it augments human decision-making rather than replacing it blindly.

Define “Action Risk Levels”:

• Low Risk: Read-only actions, internal thoughts (Auto-approve).
• Medium Risk: File modifications, draft emails (Notify-on-success).
• High Risk: Deleting data, sending payments, merging code (Require explicit approval).

Build UIs that surface the agent’s reasoning alongside the proposed action, not just the result.

Decision Framework

Choose your orchestration pattern based on task complexity and reliability needs:

Decision Heuristic: “Choose Multi-Agent Decomposition when the task requires more than 3 distinct logical steps. Use explicit approval gates for any action that affects user data or financial state.”