Agentic AI design patterns are established strategies that guide the development of AI systems capable of autonomous decision-making and task execution. These patterns provide frameworks for creating agents that can plan, reason, and interact with their environment and other agents to achieve complex objectives. Key design patterns in agentic AI include:
Reflection Pattern: This pattern enables AI models to self-evaluate and refine their outputs, mimicking human-like feedback and revision loops. It involves the AI generating an initial response, evaluating its quality, and iteratively refining it to improve accuracy and reliability.
Tool Use Pattern: Agents enhance their capabilities by leveraging external tools or resources. This pattern allows AI systems to extend their functionality beyond inherent capabilities, enabling them to perform a wider range of tasks effectively.
Planning Pattern: Incorporating planning mechanisms enables agents to strategize and sequence actions to achieve specific goals. This pattern is crucial for tasks that require foresight and structured execution, allowing agents to navigate complex problem spaces efficiently.
Multi-Agent Collaboration Pattern: This pattern involves multiple agents working together, each potentially with specialized roles, to accomplish tasks that are too complex for a single agent. Effective communication and coordination among agents are essential components of this pattern.
Agentic systems allow LLMs to dynamically control their own processes and tool usage. Unlike workflows, agents are more flexible, making decisions in real time about how to accomplish tasks rather than following a strictly predefined path.
the basic building block of a LLM-
to more about Retrieval, tools & memory- https://www.mltutor.com/post/unleashing-ai-s-potential-exploring-langchain-and-its-transformative-concepts
another categorisation of agentic design pattern based on work-flow structure -
Prompt Chaining pattern of agentic workflow -
Routing WorkFlow -
Parallelization. This workflow involves running multiple instances of LLMs simultaneously to enhance task performance. The key variations of this workflow are:
Sectioning: This involves breaking down a task into independent subtasks that can be processed concurrently by different instances of the LLM. The results from these subtasks are then aggregated to form the final output.
Voting: This variation involves running the same task multiple times using different instances of the LLM to generate diverse outputs. These outputs are then compared or voted upon to determine the most accurate or suitable result.
Parallelization can improve efficiency and accuracy by leveraging the collective capabilities of multiple LLM instances, making it a valuable strategy in complex or large-scale tasks.
In this workflow:
Orchestrator: A central LLM is responsible for dynamically breaking down tasks into smaller, manageable subtasks.
Workers: These subtasks are then delegated to multiple worker LLMs, which process them independently.
Synthesis: The orchestrator collects and synthesizes the results from the worker LLMs to produce the final output.
While agents can handle complex tasks, their implementation is often straightforward. They typically involve LLMs that use tools based on environmental feedback in a continuous loop.
Example of coding Agent design pattern ( WorkFlow)
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