AI Learning
If what most call AI Agents are just advanced LLM workflows? Not everything connected to a Large Language model can be termed as Agents. Depending upon your use case, you need to choose the right GenAI solution. So, how to identify the right solution for the use case?
LLM Workflow
- User prompt is tokenized and processed by the model architecture.
- Pretrained knowledge from large datasets or general domain data is applied.
- LLM predicts the next best text → Generates a one-shot response.
Main differences vs AI Agents:
- Purely text-generation based.
- No multi-step reasoning or external tool use.
RPA (Robotic Process Automation)
- Trigger received → Workflow identified → Automation script executed.
- Follows fixed paths (manual or scheduled).
- Interacts with applications → Logs output/status.
Main differences vs AI Agents:
- Fully rules-based, no autonomy.
- Predefined tools and workflows only.
AI Agents
- Input phase → Dynamically select tools & APIs (internal or external).
- Perform multi-step actions → Collect data via API calls / DB queries.
- Maintain memory (short-term & long-term) → Compile results → Output.
Main differences vs LLM Workflow/RPA:
- Task-oriented and tool-using.
- Maintains state and memory.
Agentic AI
- Divides input among multiple agents for parallel tasks.
- Planner agent coordinates → Data retrieval → API calls / DB queries.
- Agents communicate, re-work results, synchronize context, maintain state, update memory.
Main differences vs AI Agents:
- Multi-agent collaboration instead of single-agent execution.
- Autonomous distribution and reallocation of tasks.
Bottom line:
1. LLM Workflow = Single-shot text generation.
2. RPA = Rules + automation, no learning.
3. AI Agents = Single agent, autonomous multi-step execution with memory.
4. Agentic AI = Multi-agent ecosystem with coordinated autonomy.
Real-World Use Cases
1. LLM Workflow – Summarizing meeting transcripts.
2. RPA – Sending preformatted emails when forms are submitted.
3. AI Agents – A triage agent that scans error logs, identifies the cause, and opens a detailed Jira ticket with reproduction steps.
4. Agentic AI - A robotics warehouse system where one agent plans picking routes, another controls robot arms for item retrieval, and another coordinates packing and labeling.
Understanding the differences is just the start — the real value comes from building Agents that can thrive in enterprise environments. Thanks to Rakesh Gohel for posting this initially.
