Agents vs Workflows: Why Not Both? — Sam Bhagwat, Mastra.ai

AI Agents and Workflows Debate - The industry is currently engaged in a debate regarding the roles and effectiveness of AI agents versus workflows, sparked by differing viewpoints from Anthropic and OpenAI [1][2][3][4] - The industry should avoid dogmatic approaches, recognizing that there isn't one single "right" way to develop AI systems [5][6][7][8] - The industry should be cautious of overly complex APIs (like those relying heavily on graph theory), as they can hinder readability and team collaboration [9][10][11][12][13] Design Patterns for AI Systems - The industry needs a commonly accepted vocabulary and glossary for agentic patterns and agentic workflow patterns [14][15] - Agents can be viewed as turn-based systems, while workflows are akin to rules engines managing dependencies [16][17][18] - Workflows are gaining popularity in AI engineering due to the need to trace and manage non-determinism, which is more critical in AI than in traditional software engineering [19][20] - Balancing power and control is a key trade-off in designing AI systems; starting with powerful models and adding control where needed is a viable strategy [21][22] Composition and Implementation - Agents and workflows can be combined in various ways: agents can be steps in workflows, workflows can be tools for agents, and so on [23][24][25] - The agent supervisor model involves an orchestrator agent calling other agents as tools [25] - Dynamic tool injection, where agents are given a limited and relevant set of tools for a specific task, can improve performance [26] - Nested workflows, where a workflow is a step within another workflow, are also valuable [26][27] - Practical experience and community knowledge are currently more valuable than theoretical correctness in this rapidly evolving field [28][29]