Agent Orchestration

Multi-Agent Coordination

Decompose complex tasks into specialized agents governed by structured state-machine graphs. Designing deterministic execution flows with explicit state transitions ensures predictable agent behavior. A single “God Agent” suffers from context dilution and high error rates, whereas structured, specialized agents provide modularity and better control.

Implement a state-machine or coordinator-worker pattern where transitions are handled by code routing or schema-constrained decisions rather than open-ended dialogue. The coordinator manages high-level planning, state transitions, and task decomposition, while specialized workers execute restricted, domain-specific tasks (e.g., “Research Agent,” “Coding Agent,” “Verification Agent”). Enforce schema-guided decoding or native structured outputs (like JSON schema constraint parameters in modern model APIs) at all boundaries to guarantee type-safety and eliminate downstream format parser errors.

Anti-pattern — Unconstrained Peer-to-Peer Chat: Allowing agents to communicate autonomously with each other in an open-ended loop without strict code-level state constraints. This pattern leads to conversational infinite loops, rapid context-window saturation, high latency, and compounding errors.

Anti-pattern — Parsing-based JSON Extraction: Relying on natural-language instructions to output JSON and building custom parser retry loops to handle syntax errors. This is highly fragile. Modern APIs support native schema-guided output constraints (Structured Outputs) which guarantee schema adherence at the token generation level.

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.

Assertion-based Step Validation

Implement deterministic execution assertions at critical boundaries within the agent loop. Feeding raw test, schema, or runtime validation failures directly back into the agent’s context enables robust, closed-loop self-correction.

Rather than letting an agent continue executing steps blindly after an error, wrap key tool executions and state transitions in deterministic code assertions. For instance, when an agent writes code, compile it and run a test suite automatically; if the agent generates a configuration file, validate it against its JSON schema. If an assertion fails, immediately halt the execution loop, capture the exact traceback or validation error, and feed it back to the agent as a structured environment message (e.g., “AssertionError: Schema key ‘port’ must be an integer, got string ‘1313’”). This provides a direct, non-linguistic feedback signal that the model can use to correct its own mistake. Set a strict retry ceiling (e.g., 3–5 attempts) to prevent costly, infinite repair loops if the model repeatedly fails to resolve the issue.

Anti-pattern — Ephemeral Self-Reflection: Relying on the agent to evaluate its own success via open-ended prompts (e.g., “Review your work and confirm it is correct”). Without an external, deterministic compiler or validation runner to assert properties, the agent will frequently hallucinate that its broken output is correct. Always validate outputs with code-level checks.

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.”