Agentic AI
Definition
Agentic AI refers to artificial intelligence systems that can autonomously plan, execute, and adapt multi-step workflows to achieve defined goals. Unlike conversational AI (which responds to individual prompts) or predictive AI (which generates forecasts), agentic AI takes actions: calling APIs, reading and writing data, executing code, and coordinating across multiple systems.
An agentic AI system receives a high-level objective, decomposes it into subtasks, selects appropriate tools for each subtask, executes them in sequence or parallel, handles errors and exceptions, and delivers results -- all with minimal or no human intervention at each step.
Why It Matters
Agentic AI represents the transition from AI as a tool (human asks, AI answers) to AI as a worker (human delegates, AI executes). This is a fundamental shift in how organizations can leverage AI for operational efficiency.
Enterprise use cases include: automated report generation that gathers data from multiple systems, incident response workflows that detect issues and take corrective action, document processing pipelines that extract, validate, and route information, and operational monitoring that identifies anomalies and escalates appropriately.
The key challenge is not building agents that can act -- current LLMs are remarkably capable at tool use. The challenge is building agents that act reliably, securely, and within governance boundaries in production environments.
Key Components
- Planning -- Decomposing goals into subtasks and determining execution order
- Tool use -- Selecting and invoking external tools (APIs, databases, services) to accomplish subtasks
- Memory -- Maintaining context across multi-step workflows and across sessions
- Error handling -- Detecting failures, retrying with adjusted approaches, and escalating when necessary
- Governance -- Operating within defined permissions, logging all actions, and respecting authorization boundaries
How BAG Uses It
Our production platform operates agentic AI with 68 MCP servers and 46 autonomous skills. Agents autonomously process meeting transcripts, generate financial analytics, manage communications, monitor security posture, and coordinate cross-system workflows. We bring this production experience to every client AI engagement, ensuring agents are reliable, secure, and governed.