What is Agentic AI?

Agentic AI is a form of artificial intelligence that acts autonomously to understand goals, plan how to achieve them, and execute the steps with limited human supervision. Systems can employ different agent types as the complexity of tasks increases: simple reflex agents (rule-based reactions), model-based agents (context-aware decisions using internal world models), goal-based agents (planning-focused systems that evaluate action sequences), utility-based agents (optimization-focused evaluators that maximize desired outcomes), and learning agents (adaptive systems that improve from experience). This way, agents function more like experienced teammates, rather than passive tools that need constant human direction.

How agentic AI works

Agentic AI follows a cycle of perception, reasoning, action, and learning. Different agents can be used for different tasks, but the execution pattern remains the same.

1. Perception or input gathering – The agent collects context from your prompts, events, and data sources, such as CRM, ERP, ticketing tools, logs, and documents, so it has information about the current state before deciding what to do next. Simple reflex agents react to these inputs using predefined rules, while model-based agents inform context-aware decisions.​
2. Reasoning and planning – Using large language models (LLMs) and other reasoning components, the agent interprets your objective, breaks it into smaller tasks, evaluates options, and builds a stepwise plan that fits your policies, constraints, and success criteria. Goal-based agents evaluate action sequences to achieve defined outcomes, while utility-based agents optimize plans to maximize desired results across competing priorities. This planning step lets the agent handle tasks that require more than a single action.​
3. Tool use or action execution – The agent executes plans through API calls, workflow orchestration, and system-of-record updates. It can update tickets, adjust orders, reconcile records, or coordinate with other agents across an orchestration-based platform (like Temporal). Each action is controlled by schemas, access rules, and logging for full audit trails, so you can see exactly what happened and why. This is where the agent shifts from “thinking” to “doing.”
4. Learning and adaptation – After each round of actions, the agent reviews results and feedback, updates its state or memory, and can be tuned or retrained so future decisions align more closely with your processes and risk appetite.​ If an action fails or the context changes, it can try a different step or revise the plan. Learning agents update their strategies over time based on past experience

In an enterprise environment, this loop runs on an orchestration platform that provides durable execution, monitoring, and audit trails for thousands of concurrent agent workflows.

Key characteristics of Agentic AI

• Autonomy within boundaries – Once the objective is clear, agents can take initiative on your behalf, but they do it within defined guardrails. They only access approved data and tools, and know when to stop and hand a decision back to a human.​
• Goal-oriented behavior – Instead of scripting every step, you describe outcomes like resolving a claim, completing a customer journey, or closing a month-end process. The agent turns those objectives into an ordered set of tasks and keeps working until it reaches a valid stopping point or hits a condition requiring human input.​
• Safety and resilience at scale – Agents run on a fault-tolerant infrastructure that can resume long-running workflows after failures and handle thousands of parallel executions without losing state. This makes agents suitable for production environments, not just prototypes.
• Governance and observability built in – Enterprise agentic AI generates logs, traces, and audit records so you can always inspect actions and explain what happened. Platform-level controls enforce guardrails, approvals, and cost controls, ensuring transparency and explainability across agent operations.
• Adaptivity – Feedback loops, performance metrics, and model updates enable agents to refine behavior and align with workflows and governance practices rather than imposing new ones.​
• Multi-agent coordination – Instead of one large agent trying to do everything, you can combine specialized agents for retrieval, research, checks, and execution. An orchestration layer coordinates them and synchronizes with humans when decisions are sensitive or high-impact. This division of labor scales better than monolithic agent architectures.​

Applications and use cases

• AI agents for customer service – Customer support agents can retrieve interaction history, check policies, verify order status, and update tickets directly in your support tools. It can resolve simple issues on its own and send complex cases to a human with a clean summary, recommended next steps, and all required context.
• AI agents for digital shopping journeys – Agentic commerce deploys AI agents throughout the buying journey, from catalog browsing and availability checks to promotion application, interface adaptability, cart management, payment processing, and post-purchase support. Model-based agents maintain context across sessions, enabling personalized recommendations and simple purchase paths. By understanding shopper preferences and executing tasks autonomously, these agents reduce cart abandonment while enabling self-service completion.
• AI agents for in-store sales optimization – Sales teams or store associates can query agents for product comparisons and specifications in seconds from massive catalogs, run eligibility checks, or check available offers in natural language. Agents can also initiate interactive roleplay to help associates practice sales pitches, handle objections, and prepare closing statements.
• AI agents for coding and rapid prototyping – Vibe coding helps engineers build AI prototypes. Engineers define business problems, specify data sources, and set objectives; multiple agents then collaborate to generate workflows, test solutions, and deliver results in hours instead of months. This way, teams can validate ideas fast without heavy upfront investment.
• Agentic AI SDLC – AI-native engineering helps speed up the traditional SDLC process by prompting AI agents to execute a multi-layer, multi-step process from ideation to production. They can read requirements, suggest designs, update services across frontend, backend, and infrastructure, generate and run tests, and prepare changes for review and deployment. This helps developers spend less time on manual handoffs and boilerplate and more time on value-adding feature requests.
• AI agents for research and analytic – Research agents retrieve information from documents, dashboards, and external sources, run text-to-SQL queries using reference blueprints, and deliver structured summaries. This helps analysts (like Financial Advisors dealing with regulatory compliance) move straight to decision-making instead of spending time collecting and formatting the data. Learning agents refine query patterns over time based on analyst feedback and usage patterns.