What is an Agent Orchestrator?

A single AI agent can only do so much before it hits context-window limits, instruction drift, or domain breadth problems. Splitting work across specialist agents solves that but creates a new problem: who decides what happens when? The orchestrator is the answer. It sits above the worker agents and makes the meta-decisions: what sub-task to tackle next, which specialist should do it, what context they need, when to hand off, what to do if they fail. There are two main architectural patterns for orchestrators. (1) LLM-based supervisor: a top-level agent (usually using a frontier model like Claude 4.5 Opus or GPT-5) reads the user's goal, decomposes it, dispatches work to specialists, and integrates their responses. Flexible and handles novel situations well but expensive and can make non-deterministic routing decisions. (2) Deterministic router: a coded state machine or graph (LangGraph, Temporal, Inngest) that defines allowed transitions between agents. Cheaper, predictable, auditable, but requires you to know the workflow shape in advance. Production systems often blend both — a deterministic skeleton with LLM-based branching decisions inside. Core responsibilities of an orchestrator include seven things. (1) Goal decomposition: breaking a user request into specialist-sized sub-tasks. (2) Agent selection: picking the right specialist for each sub-task based on capabilities, current load, and past performance. (3) Context curation: deciding what subset of conversation history, memory, and prior results each specialist needs — crucial because specialists have limited context windows. (4) Hand-off management: ensuring outputs from one agent land as usable inputs to the next. (5) Conflict resolution: when agents produce contradictory results, deciding which to trust or triggering a reconciliation step. (6) Failure handling: retries, fallbacks, escalation to humans when agents get stuck. (7) Result aggregation: combining specialist outputs into the final user-facing response. Frameworks provide orchestration infrastructure. LangChain's LangGraph is the most widely adopted — graph-based orchestration where each node is an agent and edges define transitions. CrewAI takes a role-first approach — declare agents with roles and goals, declare a crew, let the manager agent orchestrate. Microsoft AutoGen emphasizes conversational agent interaction. Temporal and Inngest approach orchestration from the durable-workflow angle, treating LLM calls as steps that can be retried and audited. OpenAI's Swarm is a lightweight reference implementation. Anthropic's docs describe orchestrator-worker patterns as a recommended architecture but don't ship a framework — many Anthropic customers build their own orchestrators tailored to their use case. Orchestration also names a role. A human 'AI orchestrator' or 'agent architect' is someone who designs the agent teams: who does what, how they coordinate, what tools they have, what triggers each agent. This is an emerging job title at companies deploying agent systems at scale, blending prompt engineering, software architecture, and operations engineering. Tycoon's architecture centers on the AI CEO (Astra) as a human-facing orchestrator. A founder talks to Astra; she decides whether this request is something she should handle, delegate to a specialist (AI CMO for marketing work, AI CTO for engineering), or decompose into multi-specialist work. She manages context between specialists — each one gets a curated briefing, not the full conversation — and reintegrates their outputs before reporting to the founder. This mirrors how a real CEO works with a staff of executives, which is why the pattern feels natural to founders using the product. Under the hood, Astra is both an LLM-based supervisor (using Claude Opus 4.5 for the meta-reasoning) and a thin deterministic scaffold that enforces the non-negotiable rules (always log, always respect founder autonomy settings, always escalate ambiguous high-stakes decisions).