AI Agent and AI Workflow are related but distinct concepts in the field of Artificial Intelligence.
- AI Agent is an autonomous system capable of perceiving, deciding, and acting in its environment, often in real-time.
- AI Workflow is a structured process for creating, deploying, and maintaining AI models, typically staged and planned.
Analogy: Recipe vs Chef
- AI Workflow is like a detailed recipe: You follow the steps precisely to get a predictable dish. If an ingredient is missing, you stop or notify. The "AI" might be a smart scale telling you how much flour to add, but you're still following a set procedures.
- AI Agent is like a seasoned chef: You tell the chef to "make a delicious Italian dinner".
The chef knows various recipes, can adapt to available ingredients, troubleshoot if something goes wrong, and might even invent a new dish based on the desired goal set. The chef has autonomy and the ability to plan and execute dynamically.
- Automated Invoice Processing: OCR (AI) extracts data from invoices, which then flows through a system for validation, approval (human or AI-assisted), and payment.
- Customer Support Routing: NLP (AI) analyzes a customer's query to automatically route it to the correct department or provide a canned response.
- Predictive Maintenance: Sensors collect data (workflow step), ML (AI) analyzes it to predict equipment failure (AI step), triggering a maintenance order (subsequent workflow step).
- Content Generation for Marketing: A marketing workflow might use Generative AI to draft initial social media posts or email subject lines, which are then reviewed and refined by a human.
AI Agent:
AI agent is a software entity designed to autonomously perceive its environment, reason, plan, and take actions to achieve a specific goal with minimal human intervention. Unlike a workflow that follows a script, an agent can dynamically determine its course of action based on new information and its understanding of the goal. Agents often use sophisticated AI models (like Large Language Models - LLMs) to reason about the problem, break down complex goals into subtasks, and devise a plan to achieve them.
Examples:
- Personalized Digital Assistant: An agent that can book travel, manage calendars, find information online, and respond to emails, adapting its approach based on user preferences and real-time changes.
- Autonomous Code Generation and Debugging: An agent tasked with building a feature might research documentation, write code, run tests, identify errors, and debug the code until the feature is complete.
- Advanced Customer Service Agent: Beyond routing, an agent that can dynamically troubleshoot complex issues, access multiple internal systems, learn from customer interactions, and even initiate follow-up actions without explicit human instruction for each step.
- Research Assistant: An agent assigned to research a topic might formulate search queries, synthesize information from multiple sources, summarize findings, and even generate a report, adapting its research strategy as it discovers new information.
ref:
Gemini LLM response @ https://gemini.google.com/app/03989649287ea08e
Meta AI LLM response @ https://www.meta.ai/prompt/0dc5102b-9e90-437f-a810-01d98c5a3b7c
CharGPT LLM response @ https://chat.chatbotapp.ai/chats/-OR6XiaFDXl8y3Hz1yQ2?model=4o-mini
Automation vs. AI Workflow vs. AI Agent: Making Sense of the Buzzwords @ https://www.linkedin.com/pulse/automation-vs-ai-workflow-agent-making-sense-buzzwords-akarsh-kain-epmwc/
Automations vs AI Workflows vs AI Agents: Understanding the Key Differences @ https://www.linkedin.com/pulse/automations-vs-ai-workflows-agents-understanding-key-tyler-mcgregory-w1c8e/