About AI
What is agentic AI?
Agentic AI is artificial intelligence that has the power to reason, plan, and take action. It’s transforming how people work and how organizations win.
- Summary
- What is agentic AI?
- How agentic AI works
- What are agentic automation and orchestration?
- How automation and orchestration work together
- Where agentic AI fits in the AI and automation ecosystem
- Benefits of agentic AI and agentic automation
- Agentic AI use cases
- History of agentic AI
- The science behind agentic AI
- Risks and challenges
- How agentic AI is built and managed
- Implementing agentic AI: a practical checklist
- FAQ
Agentic AI is the autonomous intelligence that powers AI agents. Using an ensemble of advanced AI methods and algorithms, it allows agents to perceive context, reason about goals, plan step-by-step actions, and execute work across applications and systems using AI tools and integrations. With these capabilities, agents can operate effectively in unstructured environments, tackle complex problems, and support variable, multistep workflows.
Summary
Agentic AI is an advanced artificial intelligence system enabling AI agents to autonomously reason, plan, cooperate, and take coordinated action across digital systems to achieve defined goals
It’s been made possible by AI advances in generative AI (GenAI), large language models (LLMs), natural language processing (NLP), deep learning, and machine learning (ML)
It’s transformative, spurring the adoption of new enterprise operating models and brand-new modes of working
It’s valuable, already delivering significant ROI and competitive advantage for leading adopters across many different business processes, use cases, and industries. Analysts predict it will unlock trillions of dollars in global economic value as adoption spreads.
It requires new technologies, approaches, and capabilities. Critical capabilities for execution and scalability include orchestration frameworks and tools, contextual memory, dynamic tool integration, and interfaces leveraging natural language processing (NLP). Ensuring governance, observability, and security requires safe reasoning mechanisms, control-as-code, human-in-the-loop, control planes, and more.
What is agentic AI?
Agentic AI gives autonomous agents the ability to plan, make high-quality decisions, and adapt as they work toward goals. Instead of reacting to a single prompt, agents can understand context, break tasks into steps, take action through tools and systems, and improve from experience. This makes it possible for digital workers to take on more of the complex, judgment-based work that people typically perform—supporting teams, extending capacity, and accelerating outcomes across the enterprise.
Agentic AI systems can:
Understand goals and make decisions based on context and available data
Break down complex tasks into manageable step-by-step plans
Use tools, applications, and external systems to complete work
Maintain memory of relevant information and ongoing situations
Collaborate with people or other agents when needed
Learn from results to improve performance over time
Together, these capabilities allow agents to operate independently across complex workflows—handling variability, adapting to new information, and coordinating work across systems to keep long-running processes moving toward the right outcome without ongoing human intervention.
How agentic AI works
An agentic AI system is a continuous loop of perception, planning, action, and learning.
Perception
An AI-powered agent collects information from documents, applications, data sources, APIs, sensors, or other systems. It interprets this information to understand the current context.
Reasoning and planning
Based on what it perceives, the agent evaluates options and determines the steps required to achieve a goal. These initiatives may involve selecting the best sequence of actions or identifying the right tools or systems to use.
Taking action
The agent performs tasks through applications, APIs, robots, or other agents. These actions can include updating records, generating content, retrieving information, initiating workflow steps, or coordinating activity across multiple systems.
Reflecting and learning
After each action, the agent evaluates the outcome. It uses feedback and short- or long-term memory to refine its approach, improve future decisions, and maintain continuity across longer workflows.
This loop repeats continuously, allowing agents to navigate complex workflows, manage changing conditions, and operate effectively in real-world enterprise environments.
What are agentic automation and orchestration, and what are their roles in scaling agentic AI?
Agentic AI gives agents the ability to reason, plan, and act across complex workflows. But for those capabilities to operate reliably, agents need a foundation that can execute their decisions and coordinate their actions. That foundation is built on two related capabilities: agentic automation and orchestration.
Agentic automation
Agentic automation is the execution layer that turns an agent’s decisions into real work. It brings together AI agents, robots, integrations, and people to automate multistep tasks across systems. Agents interpret context and determine what needs to happen next. Robots perform consistent, high-fidelity actions across screens, APIs, and applications. People provide direction, approvals, and specialized judgment where oversight is required through human-in-the-loop controls. A defining characteristic of agentic automation is its ability to operate across diverse enterprise systems—legacy and modern, structured and unstructured.
Orchestration
Orchestration is the coordination capability within agentic automation that governs how agents, robots, and people work together. It defines roles, permissions, sequencing, and handoff rules across a workflow. It ensures actions are observable, decisions are auditable, and behavior aligns with enterprise policies and governance requirements.
Through orchestration, organizations can manage multi-agent collaboration, long-running processes, cross-system interactions, and the points where human oversight is required—ensuring that autonomous activity is safe, predictable, and aligned with business goals.
How automation and orchestration work together
Orchestration is a core part of agentic automation, and the two work together to enable agentic AI to operate effectively across the enterprise.
Agentic automation provides the ability to execute work across systems through robots, integrations, and AI-driven actions.
Orchestration coordinates those actions, ensuring they occur in the right sequence, with the right permissions, and with the visibility and guardrails needed for safe and compliant operation.
Together, they provide the structure and control that allow AI agents to plan, act, and deliver complex outcomes at scale.
Where agentic AI fits in the AI and automation ecosystem
Agentic AI operates within a broader landscape of AI models and techniques, automation tools, and coordination and control capabilities. Understanding this ecosystem clarifies how agentic AI works, what it depends on, and how it interacts with other enterprise technologies.
AI capabilities: the cognitive building blocks
Generative AI (GenAI)
GenAI produces text, code, summaries, or other content. Agentic AI may use generative outputs as part of a larger plan but extends beyond generation to also include decision making and taking action.
Traditional AI, machine learning, and deep learning
Traditional AI, machine learning, and deep learning models support tasks such as classification, prediction, pattern recognition, and optimization. Deep learning models, in particular, excel at interpreting unstructured data like text, images, and audio. Agentic AI incorporates these capabilities within broader workflows that require context-sensitive planning, decision making, and action across multiple systems.
Reasoning models
Reasoning models support structured problem-solving and multistep thinking. Agentic AI uses these capabilities to evaluate options and determine appropriate next steps.
Natural language interfaces
Natural language interfaces make AI accessible by allowing people to express goals in everyday language. Agentic AI can interpret these inputs, although decision making is driven by its planning and control logic.
Operational entities and roles: the actors performing the work
AI agents
AI agents apply agentic AI and embedded control logic to perceive context, make decisions, and take action. Control logic may include explicit rules, constraints, and policies that shape behavior and provide predictable, auditable decision paths. Agents may work individually or in collaboration with other agents, robots, and people.
Specialized agents
Specialized agents are designed for focused roles or domain-specific tasks. They apply targeted logic or expertise to handle distinct steps in a workflow—such as extracting data, validating inputs, assessing risk, or coordinating handoffs.
Deep agents
Deep agents are a new class of agentic systems—multistep and goal driven—capable of planning, retaining context, recovering from errors, and coordinating subagents. Unlike shallow task-level agents, deep agents handle long-horizon tasks and adapt to real-world complexity, operating more like autonomous digital coworkers.
Multi-agent systems (MAS)
Multi-agent systems involve multiple agents that share context and coordinate tasks within a workflow. MAS architectures support specialization, distributed problem solving, and resilience, allowing agents to hand off work or operate in parallel. Agentic AI supports these patterns through interoperable planning and communication capabilities.
Execution and automation technologies: tools and resources for taking action
Robotic process automation (RPA)
RPA enables robots to perform structured, repeatable actions at speed and scale. Agents call on robots to execute reliable UI or system interactions and deterministic and structured processes, while agents take on steps that require reasoning or adaptation.
System integrations and API tools
Agents use integration technologies such as APIs, connectors, and system actions to perform work inside enterprise applications. These capabilities allow agents to retrieve data, update records, and complete tasks across platforms.
Prebuilt agentic AI solutions
Prebuilt agentic AI solutions provide ready-made, domain-specific capabilities that teams can deploy quickly to accelerate adoption. These packaged agents and workflows offer tested logic, integrations, and guardrails, giving organizations a fast way to introduce complete agentic workflows without creating every component from the ground up.
Coordination and governance: enterprise controls that enable scale
Orchestration
Agentic orchestration governs how agents, robots, and people work together across workflows. It defines collaboration patterns, permissions, sequencing, and oversight to ensure safe and coordinated execution. It also supports multi-agent systems by managing shared context and aligning decisions with enterprise policies.
Guardrails and policy controls
Guardrails and policy controls provide the enterprise rules that guide agent behavior. They enforce access permissions, safety checks, decision boundaries, and escalation conditions—ensuring that agents operate within approved policies with minimal human intervention and adapting those rules as processes evolve.
Monitoring and oversight
Monitoring and oversight offer real-time visibility into agent operations through analytics dashboards, audit trails, and performance checks. These capabilities help teams validate system behavior, detect drift or unexpected outcomes, and maintain reliable, compliant operation across workflows.
What are the benefits of agentic AI and agentic automation?
Agentic AI expands the scope of work that can be automated by enabling systems to interpret context, make decisions, and adapt to real-world conditions. Combined with enterprise automation, these capabilities deliver measurable impact.
Increased efficiency and throughput
Agents can manage multistep tasks, coordinate activity across systems, and involve people only when needed, reducing manual effort and increasing process throughput.
Higher accuracy and consistency
Agents use structured reasoning, defined policies, and real-time information (optimally, enterprise/proprietary data) to make repeatable, context-aligned decisions. Robots provide precise system execution, reducing variation and improving reliability.
Faster cycle times
Agentic automation accelerates workflows by reducing wait states, triggering follow-on steps automatically, and moving work forward without prompts.
Improved customer and employee experiences
Agents can support people by rapidly gathering data from a wide range of systems and sources, analyzing it, and presenting targeted, compliant recommendations. Service representatives can respond to customers more quickly and with less effort, while customers experience fast, responsive, and reliable interactions.
Built-in governance and control
Many agentic systems include code-as-control, allowing explicit rules and behavior constraints, decision and escalation paths, and auditing and monitoring to be built into the agents operating code.
Better use of skilled talent
Agents handle operational load, allowing employees to focus on complex decisions, innovation, and customer interactions.
What are the highest-impact agentic AI use cases today?
Agentic AI delivers the most value in workflows that blend structured tasks with decision making, variability, and coordination across systems. Examples include:
Streamlining insurance claims and underwriting
Agents gather data, analyze claims, check policy rules, prepare communications, and escalate exceptions to adjusters. In underwriting, agents assemble required data and generate draft recommendations.
Optimizing logistics and supply chain operations
Agents analyze transportation, inventory, and demand signals; identify delays; coordinate rerouting; and initiate system updates through robots or APIs.
Enhancing financial decision support and risk management
Agents gather data from internal and external sources, analyze trends, evaluate exposure, identify anomalies, and prepare draft insights or summaries for advisors.
Strengthening cybersecurity and incident response
Agents monitor activity across systems, detect anomalies, surface potential threats, and draft incident summaries. They can trigger follow-on steps, coordinate escalations, and support faster, more consistent security response.
Accelerating drug discovery and healthcare workflows
Agents review literature, extract insights, organize inputs, support hypothesis generation, and coordinate administrative workflows such as pre-authorizations or case routing.
Improving customer support and case resolution
Agents classify requests, gather context, draft responses, coordinate actions across systems, and escalate judgment-based steps to humans.
Enhancing testing and quality assurance
Agents generate test cases, analyze results, identify root causes, compare outcomes against known issues, and prepare summaries for engineering teams.
Accelerating software development
Agents assist with code generation, review, refactoring, dependency analysis, and documentation. They coordinate multistep tasks such as identifying required changes, proposing updates, validating outputs, and preparing pull requests.
What is the history of agentic AI?
Agentic AI builds on advances in AI that have unfolded over several years:
2017: The Transformer architecture introduced a more effective method for handling context, enabling the development of large language models.
2018–2021: Researchers trained Transformers on massive datasets, leading to models capable of summarization, reasoning, and conversation.
2022: Conversational AI improved significantly through alignment techniques that made models safer, more helpful, and easier to use.
Today: By combining language understanding with planning, memory, and tool use, AI systems can now move beyond generating responses to pursuing goals and completing tasks—paving the way for agentic AI and enterprise-scale autonomy.
What is the science behind agentic AI?
Agentic AI draws on multiple branches of artificial intelligence:
Language and action models
Large language models (LLMs) process and generate natural language, enabling agents to interpret instructions, analyze content, and interact with users. Small language models (SLMs) provide lightweight, efficient language capabilities for simpler or on-device tasks. Large action models (LAMs) extend these abilities by supporting planning, tool use, and multistep actions. Together, these model types give agents the reasoning, interpretation, and execution capabilities needed across a wide range of workflows.
Planning and decision models
Planning models help agents sequence tasks, set intermediate steps, and choose among possible actions.
Reinforcement learning
Reinforcement learning enables agents to adjust behavior based on outcomes and optimize actions through trial and feedback.
Memory systems
Short-term and long-term memory models help agents maintain context over time and handle multistep or long-running workflows.
Retrieval-augmented generation (RAG)
RAG combines model generation with retrieval from trusted enterprise data sources, enabling agents to ground their decisions and outputs in current, verifiable, and proprietary information. Agents can use RAG to reduce hallucinations, access domain-specific knowledge, and take actions based on the most relevant and up-to-date content available.
Tool-use models
Models capable of recognizing when external tools or systems are needed allow agents to use APIs, databases, applications, and robots to complete tasks.
These capabilities combine to support the perceiving, reasoning, planning, acting, and learning cycle that defines agentic AI.
What are the risks and challenges of adopting agentic AI?
Agentic AI’s flexibility and autonomy introduce new considerations for governance, security, and reliability. Organizations must ensure that these systems behave safely, transparently, and within defined boundaries.
Autonomy and oversight
Balancing autonomy with human oversight is essential. Clear rules, policies, and escalation paths help ensure that agents act within defined limits.
Transparency and reliability
Agents may generate results that require validation or explanation. Audit trails, evaluation frameworks, and continuous monitoring improve traceability and reduce uncertainty.
Security and privacy
Agents often access sensitive data. Strong identity controls, access policies, and monitoring are essential to prevent unauthorized use and maintain data integrity.
Coordination and alignment
As multi-agent systems become more common, maintaining shared context and preventing divergent behavior is critical. Orchestration frameworks help maintain alignment.
Model and tool integrity
Agents rely on many components, including models, APIs, and third-party tools. Regular testing, validation, and provenance tracking reduce the risk of incorrect or unsafe behavior.
How agentic AI is built and managed
Deploying agentic AI requires aligning design, orchestration, integration, and oversight so agents can operate consistently and safely across the enterprise.
Agent design and build
Agents are designed around a clear goal, access to the right data, and defined boundaries for decision making. Embedded control logic shapes behavior and ensures consistency.
Orchestration and security
Agents coordinate with other agents, robots, and people through orchestration layers that manage collaboration, permissions, and guardrails. Every action is traceable, and every decision is auditable.
Integration
Agents connect to enterprise systems using APIs, forms, robots, and applications. Integrations allow agents to retrieve information and act within existing workflows without disruption.
Observation, testing, and validation
Continuous observation, testing, and feedback support safe scaling. Monitoring tools track decisions and outcomes, while validation frameworks confirm expected behavior before deployment.
Implementing agentic AI: a practical checklist
Deploying agentic AI is not about replacing workflows but rather redesigning them to take advantage of autonomous intelligence. This checklist outlines a practical approach.
Select a goal-oriented process
Choose a process where reasoning or context-sensitive decisions matter; define outcomes and success criteria.
Establish governance and guardrails
Define access, oversight, and escalation policies before development begins.
Prototype and test
Pilot agents on a narrow workflow task to observe how they plan, act, and learn.
Orchestrate and integrate
Connect agents to existing systems and tools through orchestration.
Scale and improve
Expand gradually while monitoring performance, drift, and outcomes, applying continuous improvement.
FAQ
Q: What is agentic AI in simple terms?
A: Agentic AI is the intelligence that enables AI agents to understand context, make decisions, and take action to achieve goals. It allows AI to operate beyond single prompts and perform multistep work autonomously.
Q: How is agentic AI different from generative AI?
A: Generative AI creates content, while agentic AI uses reasoning, planning, and action capabilities to complete tasks and pursue defined objectives. They are complementary and often work together.
Q: What is an AI agent?
A: An AI agent is a software actor that performs work in applications and systems using agentic AI and embedded control logic. Agents can coordinate tasks, use tools, and collaborate with people or other agents.
Q: What is agentic automation?
A: Agentic automation is the execution of work through AI agents, robots, and people. It supports end-to-end workflows that combine decision making with structured system actions.
Q: What is agentic orchestration?
A: Agentic orchestration manages how agents, robots, and people work together across a workflow. It defines roles, permissions, sequencing, and guardrails to ensure controlled and compliant execution.
Q: Can agentic AI work with existing enterprise systems?
A: Yes. Agents and robots can operate through user interfaces, APIs, data services, and other integration points, enabling automation across both legacy and modern systems.
Q: What types of work are best suited for agentic AI?
A: Processes that mix structured tasks with decision points, variability, or coordination across multiple systems benefit the most. Examples include claims processing, case management, underwriting, and operational analysis.
Q: Do agentic AI systems require human oversight?
A: Yes. Even autonomous systems operate within defined policies, constraints, and escalation paths. Human review is used for exceptions, approvals, and high-judgment scenarios.
Q: Are multi-agent systems supported?
A: Yes. Agentic AI supports multi-agent patterns where agents share context, divide tasks, or collaborate to complete complex workflows.
Q: How do agents stay aligned with enterprise policies?
A: Agents follow defined control logic, guardrails, and orchestration rules. Audit trails and monitoring ensure decisions remain consistent with policy and governance requirements.
Q: What are the main risks of adopting agentic AI?
A: Key risks include autonomy misalignment, data access concerns, transparency challenges, and integration vulnerabilities. Governance, orchestration, and continuous monitoring help mitigate them.
Q: How can organizations start implementing agentic AI?
A: Begin with a goal-oriented process, establish guardrails, build a small prototype, integrate through orchestration, and scale gradually while monitoring performance and behavior.
