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agentic-patterns

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🗂️ AI & Agents · View mindmap

Agentic Patterns

Agentic patterns are recurring architectural and behavioral structures that emerge in AI agent systems. These patterns represent standardized solutions to common problems in agent design, such as how agents coordinate with one another, process information, execute tasks, and manage state across operations. Rather than prescriptive frameworks, agentic patterns describe observed configurations that have proven effective for specific use cases and constraints.

Multi-Agent Coordination

One significant class of agentic patterns involves the interaction and coordination between multiple agents. These patterns address challenges such as task delegation, consensus-building, and sequential or parallel execution of agent-driven workflows. Multi-agent coordination patterns enable complex problem-solving by allowing specialized agents to operate on different aspects of a problem while maintaining coherent overall behavior.

Document and Information Processing

Another common pattern class involves agents working with documents, structured data, and information retrieval. These patterns typically combine input processing, semantic analysis, and iterative refinement to extract or transform information. Document processing patterns are widely used in research workflows, content generation, and knowledge extraction tasks.

Code Execution and Development Workflows

Agentic patterns also encompass structured approaches to code generation, testing, and execution. These patterns define how agents can safely interact with code execution environments, validate outputs, and iteratively improve code-based solutions. Such patterns are central to agent-assisted software development and technical problem-solving workflows.

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