📌 After months of feedback and iteration, we are finally releasing our first technical cohort, “AI Agent Engineering”
🔗 Enrol here: https://lnkd.in/gDEPcXBB
Comparing AI Agents with the modern GenAI architectures
Let us understand the core difference between them…
As GenAI systems become more intelligent, we are looking at a rapid rate of progression.
📌 Starting from level 1, lets see how systems have progressed so far:
1. LLM (Large Language Model)
– Context-Free Generation: Produces text purely from prompt input without external retrieval.
– Fast & Simple: Easy to deploy with low complexity, but limited in context understanding or integrating new data sources.
2. RAG (Retrieval-Augmented Generation)
– Knowledge-Enhanced: Combines LLM output with real-time retrieval from external sources for more accurate, up-to-date responses.
– Data-Dependent Precision: Excels at Q&A and knowledge tasks but is sensitive to the quality and structure of underlying data sources.
3. AI Agent
– Autonomous Task Execution: Uses planning, reasoning, memory, and tool integrations to complete workflows that need decision-making.
– Goal-Oriented Automation: Ideal for well-defined tasks like multi-step data processing or tool-based operations needing structured plans.
4. Agentic AI
– Multi-Agent Collaboration: Deploys multiple specialized agents that coordinate, divide labor, and even negotiate to handle complex problems.
– Adaptive & Persistent: Supports memory, feedback, and reasoning across agents to tackle large-scale tasks requiring ongoing strategy.
📌 Progression
1. LLM Workflow:
– Begins with next-word prediction on static training data—ideal for simple text generation and chatbots with limited context.
2. RAG:
– Enhances LLMs by retrieving real-time external knowledge, grounding responses with accurate, up-to-date information.
3. AI Agents:
– Introduces planning, memory, and tool use to autonomously execute multi-step workflows with reasoning.
4. Agentic AI:
– Evolves into a collaborative multi-agent ecosystem where specialized agents coordinate, share memory, and divide tasks to solve complex problems together.
📌 Use-Cases
1. LLM:
For generating text or answering simple, general questions without needing external data.
2. RAG:
For retrieving and summarizing up-to-date, domain-specific knowledge during a conversation.
3. AI Agent:
For automating single-user tasks that need planning and tool use—like research assistance, report generation, or workflow automation.
4. Agentic AI:
For managing complex, multi-step, multi-user processes where multiple specialized agents coordinate as an ecosystem.
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