The article argues that traditional AI agent implementations, which rely on single-step language model calls, are failing. The future of production-ready AI depends on adopting orchestrated, multi-step “agentic workflows.” These patterns transform isolated AI calls into coordinated systems of autonomous, adaptive, and self-improving agents, which is critical for overcoming the high failure rate in enterprise AI.
Sequential Intelligence Patterns
These patterns decompose complex problems into a linear series of steps. Prompt Chaining passes the output of one step as the input to the next, which is ideal for multi-turn conversations. Plan and Execute involves an agent creating a multi-step plan, executing it, and adjusting based on outcomes, providing resilience for business process automation.
Parallel Processing Patterns
These patterns focus on efficiency by running tasks concurrently. Parallelization splits a large task into independent sub-tasks for multiple agents, drastically reducing resolution time for tasks like code reviews or candidate evaluations. The Orchestrator-Worker pattern uses a central agent to break down tasks and assign them to specialized worker agents, synthesizing the results for complex research or coding.
Intelligent Routing Patterns
These patterns enable dynamic and specialized task handling. Routing classifies input to direct it to the most appropriate specialized agent, which is key for multi-domain customer support. The Evaluator-Optimizer pattern creates a feedback loop where one agent generates solutions and another evaluates and suggests improvements, continuously enhancing quality.
Self-Improving Systems Patterns
These patterns allow agents to learn and adapt over time. Reflection enables an agent to review its own performance and learn from errors. Other patterns like Rewoo and Autonomous Workflows allow agents to operate in continuous loops, using feedback and environmental signals to perpetually self-improve with minimal human intervention.
Key Conclusion and Takeaways
The core message is that shifting from single-step AI calls to orchestrated agentic workflows is essential for building scalable, resilient, and intelligent automation. These patterns allow AI systems to tackle complex problems that single agents cannot, while also enabling continuous improvement through built-in feedback loops. Best practices for implementation include designing for modularity, integrating with external tools and APIs, and focusing on creating robust feedback mechanisms.
Mentoring question
Considering a complex task or business process in your field, which of the nine agentic workflow patterns could be combined to create a more robust and autonomous solution than a single AI model could achieve on its own?
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