Five Essential AI Agent Workflow Design Patterns

Prompt Chaining

Prompt Chaining links a series of prompts so that the output of one LLM call becomes the input of the next, turning a complex task into a fixed, step‑by‑step pipeline.

Example: To write a report, the first prompt generates an outline, the second fills in details, and the third refines the language.

When to use: Tasks with a clear, ordered sequence such as multi‑part Q&A or structured analysis benefit from this pattern because each step is simpler and more precise.

Routing

Routing uses an LLM (or a simple classifier) to analyse incoming input, classify it, and direct it to a specialised sub‑agent rather than handling everything with a single generic prompt.

Example: A customer email is routed either to a “refund processing” agent or a “technical support” agent, allowing each to focus on its domain.

When to use: Situations where requests belong to distinct categories (e.g., summarisation, translation, classification) and require different handling.

Parallelization

Parallelization enables an agent to execute multiple tasks simultaneously and merge the results. It appears in two forms:

Sectioning: Split a task into independent subtasks that can run in parallel, such as analysing different product features at once.

Voting: Run the same prompt multiple times (varying parameters or models) and aggregate the answers via majority vote or ensemble methods.

Example: Simultaneously generate a creative story, a humorous poem, and a factual summary on the same topic, then concatenate the outputs.

When to use: Large or time‑sensitive tasks, or when diverse viewpoints are desired.

Orchestrator‑Worker

The Orchestrator‑Worker pattern dynamically delegates subtasks: a central LLM (the orchestrator) receives the input, decomposes it in real time, and assigns each subtask to worker agents that run in parallel.

Example: An orchestrator analyses a bug report, decides that three separate files need fixing, and sends each file to a dedicated code‑writing LLM. The orchestrator then merges or reviews the results.

When to use: Complex workflows where the full set of subtasks cannot be predicted beforehand, such as multi‑file code changes or comprehensive reports.

Evaluator‑Optimizer

Also known as a generator‑critic loop, this pattern pairs two agents: a generator LLM produces a response, and an evaluator LLM scores or critiques it, feeding the feedback back to the generator for improvement.

Example: An initial code snippet is generated, then an automated reviewer checks style and correctness. If issues are found, the evaluator’s feedback guides the generator to produce a revised version.

When to use: Tasks that require iterative refinement to meet strict quality standards, such as creative writing or precise code generation.

Why These Patterns Matter

Understanding and applying these patterns is crucial for building reliable, maintainable AI systems. Modular patterns let teams test and optimise individual components without affecting the whole, control latency and cost, and adapt to evolving requirements. Modern frameworks like LangChain (and its LangGraph extension), CrewAI, and Microsoft’s AutoGen library provide ready‑made modules for these patterns, allowing developers to focus on business logic rather than low‑level orchestration.