Agentic AI

Agentic AI is an approach where autonomous software AI agents pursue goals, choose actions, and adapt based on outcomes. Instead of following a fixed sequence, an agent reasons about the state of the world, takes a step, evaluates the result, and decides what to do next. Multiple specialist agents can work together, each with a clear role, to deliver outcomes with speed, quality, and traceability.

What is an Agentic AI Agent?

An Agentic AI agent is a goal-driven component that can:

• understand a task and constraints

• plan a next step using tools and context

• act by calling APIs for data, querying data, perform tasks or drafting content

• record what it did and why for audit and learning
  
  

Multi-Agent Architecture

In an agentic system, several focused agents collaborate:

• Orchestrator: assigns goals, tracks progress, resolves conflicts

• Research and Data Agents: find information, extract facts, validate sources

• Builder and Operations Agents: create artifacts, run jobs, integrate with systems

• Reviewer and QA Agents: evaluate outputs against policies and acceptance criteria

• Safety and Policy Agents: enforce permissions, PII handling, and compliance rules

This structure is composable. New agents can be added or swapped without redesigning the entire system.

Core Principles

• Outcome orientation: optimize for the defined goal, not a predetermined checklist

• Adaptive planning: update the plan after each result and handle uncertainty explicitly

• Tool use: combine model reasoning with enterprise tools, APIs, and data stores

• Reflection: embed tests, critics, and confidence scoring so the system can self-correct

• Human in the loop: route decisions for review when risk or ambiguity is high

• Observability: maintain rich logs of prompts, actions, evidence, and decisions
  
  

Lifecycle at a Glance

1. Define the goal, constraints, and success criteria

2. Draft an initial plan and select the agents and tools to use

3. Execute steps, observe results, and adapt the plan

4. Review, approve, and deliver the outcome

5. Capture telemetry and lessons to improve future runs
   
   

Benefits for the Enterprise

• Resilience: continues to make progress when inputs change or systems fail

• Speed with control: faster iteration while preserving approvals and guardrails

• Quality through specialization: small focused agents outperform one generalist flow

• Reusability: agents become building blocks for new services and processes

• Auditability: every action and rationale is traceable for compliance and RCA

Why it is not RPA

• RPA executes a fixed script that expects stable inputs. Agentic AI adapts the plan when conditions change.

• RPA relies on explicit rules and screen flows. Agentic AI uses reasoning, tools, and data to choose the next best step.

• RPA needs the end-to-end path defined in advance. Agentic AI only needs goals, constraints, and guardrails.

• RPA struggles with ambiguity and novel cases. Agentic AI routes for review, explores alternatives, or learns from feedback.