Tayyab BilalLinkedIn AIMarch 8, 20265 min read
What Is Agentic AI and When Does Your Business Need It
In summary
- Agentic AI refers to systems where an LLM takes a sequence of actions using tools to complete a goal autonomously.
- The right use cases involve multi-step workflows that currently require human coordination across multiple software systems.
- Agentic systems are not appropriate for tasks that require real-time sub-100ms response or perfect determinism.
- LangGraph and similar orchestration frameworks add auditability through explicit state machines rather than opaque prompt chains.
- The economic case for agentic AI is strongest when you can quantify the human hours currently spent on the workflow it replaces.
Agentic AI refers to systems where an LLM takes a sequence of actions using tools to complete a goal autonomously.
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Tayyab BilalLinkedIn
Tayyab is a machine learning engineer, backend developer, and DevOps engineer. He's built AI systems that cut inference costs by 80% and run at 99.5% uptime in production, engineered APIs, databases, and cloud infrastructure on AWS for live platforms, and handles deployment pipelines end to end — so nothing stalls waiting for a separate DevOps team. His work spans multi-agent orchestration, RAG pipelines, quantized LLM deployment, and computer vision.