Agentic Workflows and Harnesses

Definition

Agentic Workflows and Harnesses

An agentic workflow is a multi-step process in which a model plans, selects tools, observes results and adapts its next action toward a goal. The harness is the software layer that supplies tools, state, memory, permissions, retries, tracing, stopping conditions and user approvals; it is often as important as the underlying model for reliability and safety.

Why It Matters

An agentic workflow uses one or more model calls to perform a multi-step task. Some workflows have a fixed graph; more autonomous agents repeatedly select their own next action within a harness.

The agent harness manages instructions, tools, state, memory, permissions, stopping conditions, retries, traces and human intervention. Much of an agent’s practical capability comes from the harness, not only the model.

Reusable workflow patterns include prompt chaining, routing, parallelisation, orchestrator-worker, evaluator-optimizer and human approval. State records what is true during a workflow; memory makes selected information available across steps or sessions.

Failure mode

The recurring risk is treating this concept as a label rather than an engineering decision with trade-offs, evidence needs and operational consequences.

Evaluation check

Ask what observable evidence would show that this concept has been applied well in a real system, repository or learning exercise.

Related Concepts

• The agent loop
• Multi-agent patterns

Further study: OpenAI, Building agents, and Anthropic, Building effective agents.