Orchestration Patterns

Build from simple to complex — a single LLM call first; a workflow when the steps decompose cleanly into fixed subtasks; an autonomous agent only when you genuinely need dynamic decisions and flexible execution paths. Agent systems trade latency and cost for better performance, so weigh the trade carefully.

Orchestration is how “context and tools” are organized inside the Harness — it decides how context flows between LLM calls, how tools are scheduled, and whether the execution path is fixed in advance or generated dynamically. Across dozens of teams building LLM agents, the most successful implementations tend not to use complex frameworks but simple, composable patterns.

Follow a “simple to complex” principle. Start with a single LLM call — if optimizing the prompt and a few in-context examples solves the problem, do not introduce an agent at all. When the work needs multiple steps that decompose cleanly into fixed subtasks, reach for a workflow. Only when the work needs dynamic decisions and flexible execution paths should you use an autonomous agent. Remember that agent systems generally trade latency and cost for better task performance — weigh whether that trade is worth it.

Workflows: Deterministic Orchestration

A workflow orchestrates LLMs and tools through predefined code paths. The execution path is deterministic, designed up front by the developer — what each step does and where it goes next is fixed in code; the LLM only understands and generates within each node.

Take a flight-booking agent. A workflow might be four fixed nodes: verify identity → search available flights → complete payment → confirm booking. Each node can use an LLM internally (to understand the user’s travel needs, say), but the order between nodes is hard-coded — the system will not book a seat before payment, nor search flights before verifying identity.

Workflows have two core advantages. First, strict process control: the developer can guarantee that critical steps are not skipped or reordered — rules like “no booking before payment” are enforced by code, not left to the LLM’s judgment. Second, safety: because the path is fixed, a prompt injection or a model mistake can at most affect processing inside the current node, not jump the agent to a branch it should not run — the attack surface is confined to a single node. The main limitation is inflexibility: when a situation arises that the predefined path did not anticipate (the user wants to change the flight during payment), the fixed node path cannot adapt and can only fall back to a preset exception branch or hand control back to a human.

Autonomous Agents: Dynamic Decision-Making

When a workflow’s fixed path cannot meet the need, we turn to an autonomous agent. Its defining difference from a workflow is that the execution path is not predefined but decided by the agent in real time, based on environmental feedback.

Still with flight booking: an autonomous agent needs no four fixed nodes. The user says “book me a flight to Shanghai next Wednesday,” and the agent decides on its own to search flights, discovers it needs to log in, so verifies identity first, comes back to search, finds the cheapest flight requires a connection, proactively asks whether that is acceptable, the user says no, and the agent adjusts its search — and so on.

This means an autonomous agent needs the ability to plan on its own, to recognize failure and adjust strategy rather than simply stopping on error. But autonomy is not unlimited — you must design explicit stop conditions (task complete, max iterations reached, or an unrecoverable error), or the agent easily falls into loops or over-execution. Structurally, an autonomous agent is just an LLM using tools in a loop, driven forward by continuous environmental feedback — precisely the ReAct loop. It suits open-ended problems where the number of steps cannot be predicted: coding agents on SWE-bench, computer use, research tasks that iterate through search and analysis. The cost is higher, with a real risk of compounding errors, so deploy in a sandbox, set appropriate guardrails and monitoring, and add human checkpoints at critical decisions.

Choosing and Mixing the Two

In practice, workflows and autonomous agents are not either/or — many systems mix them: critical, compliance-bound flows use a workflow for reliability, while parts that need flexible judgment switch to autonomous mode. Visual workflow platforms like n8n let you use workflow nodes and autonomous-agent nodes in the same system.

As the “model is the agent” trend deepens, a framework’s core value is no longer just “orchestrating LLM calls” — models decide more on their own, but the context management, tool ecosystem, safety constraints, and error recovery built around the model (Harness engineering) matter more, not less. When choosing a framework, the key consideration is not the framework’s own complexity but whether it lets you focus on business logic with the least abstraction in the way.

Was this page helpful?
Edit on GitHub