Agentic Workflow Design Patterns
As AI systems move beyond simple chatbots, the real challenge isn’t what the model can say, it’s how we structure work around it. This is where agentic workflow design patterns come in. Think of them as proven ways to organize AI-driven tasks so systems are reliable, scalable, and useful in the real world. Below are five core patterns to consider when building serious AI Agentic Workflows.
1. Prompt Chaining
Prompt chaining is the most straightforward pattern. A large task is decomposed into smaller, fixed steps, where the output of one step feeds into the next. This works well when the process is predictable. For example, extracting data first, summarizing it next, and finally generating a response. It’s simple, readable, and easy to debug, which makes it a great starting point for many AI workflows.
2. Routing
Routing is about decision-making at the front door. Instead of sending every request through the same logic, the system first decides where the input should go. As an example, an expense receipt might be routed to a financial parser, while a customer email might be routed to a sentiment analyzer. This separation of concerns keeps systems clean and prevents one giant prompt from trying to do everything.
3. Parallelization
Some tasks don’t need to wait for each other. Parallelization allows multiple subtasks to run at the same time. For example, validating data, checking policy compliance, and generating summaries concurrently. This pattern improves performance and responsiveness, especially in time-sensitive workflows like approvals or monitoring systems.
4. Orchestrator–Worker
This is where agentic systems start to feel powerful. Instead of hardcoding the flow in advance, an orchestrator (often an LLM) dynamically decides which workers to invoke, in what order, and with what inputs. The key idea here is flexibility: the system adapts its plan based on the task at hand, rather than following a rigid script. Another thing is remember is that at this point, the LLM is in-charge, not the code. This pattern is common in complex business workflows where requirements change case by case.
5. Evaluator–Optimizer
Trust matters. In this pattern, one model produces an output, while another model evaluates it checking for correctness, quality, or compliance. If needed, the output is refined and improved. This mirrors how humans work: draft, review, revise. It’s especially valuable in high-stakes scenarios like financial decisions, legal analysis, or automated reporting.
To put the above in practice, see the following "Planning a Vacation" scenario which is managed using the above design patterns:
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