Multi-Agent Orchestration for Business: A Reliability-First Guide

A single assistant with a clear contract and the right tools is easier to maintain. OpenAI recommends maximizing one agent before splitting, because additional agents introduce overhead. Split when one prompt contains too many conditional branches, similar tools are repeatedly confused, separate domains need distinct instructions, or parallel work creates a meaningful speed or coverage benefit.^[1]

Anthropic describes several patterns that are often called multi-agent: routing sends a task to one specialist; parallelization runs independent subtasks; orchestrator-workers lets a lead dynamically create subtasks; evaluator-optimizer adds a critique loop. Choose the smallest pattern that addresses the measured failure.^[2]

The lead receives the user goal and remains accountable for the final result. Before delegation, it defines the work product, evidence boundary, acceptance criteria, and subtask interfaces. Each specialist receives only the context required for its assignment and returns a structured result: findings, sources, assumptions, confidence, unresolved questions, and a completion signal.

Specialists should not silently create new goals or delegate again unless the architecture explicitly allows it. A shallow graph is easier to observe and limits cascading failure. When specialists disagree, the lead should preserve the conflict, compare source quality, and either resolve it through a deterministic rule or ask the user for judgment.^{[6] [4]}

Sending the complete conversation and every file to every specialist increases token cost and gives each worker irrelevant instructions to interpret. Build a task-specific context packet instead. Preserve immutable user constraints and approved facts, then add only the sources and decisions needed for that subtask.

Deduplicate research before synthesis. If two specialists cite the same source, one citation should not become two independent pieces of evidence. Normalize units, dates, entity names, and confidence labels. Track which statement originated from which specialist so a reviewer can trace a disputed recommendation.

A multi-agent run needs independent reasons to stop. Completion can mean required artifact fields exist, deterministic calculations reconcile, source coverage exceeds a threshold, or an approved tool confirms the expected state. Failure can mean a budget is exhausted, a critical source is unavailable, a specialist repeats the same error, or the task crosses an approval boundary.

Evaluate the final outcome and the trajectory. The final memo can look good even if a specialist accessed an unnecessary connector or ignored an instruction. Conversely, a longer trace may be appropriate for a difficult task. Agent evals should combine deterministic, model-based, and human graders according to what each can judge reliably.^{[3] [6]}

Kona routes a Workspace task to a lead and no more than two specialists. The user can also select or @mention a specialist directly, which bypasses unnecessary routing when intent is already clear. Capability declarations keep web, code, canvas, and connectors attached to explicit assistant configurations.

The run record captures routing reason, participating assistants, status, verification, and operational telemetry. The lead synthesizes one answer and does not treat specialist output as automatically verified. External mutations remain behind approval. This design favors controlled specialization over unbounded agent proliferation.^{[8] [9] [10]}