What is agentic AI?
Agentic AI describes AI systems that exhibit agency, meaning the ability to initiate and execute actions toward a goal rather than simply generating responses to questions. Understanding what makes AI truly agentic helps public sector leaders identify where agents can have the greatest impact and how to use them responsibly.
Familiar examples of AI agents
Many public sector employees have already interacted with AI agents, even if they didn’t call them that. Chatbots on public sector websites that answer questions about permit applications or benefit eligibility are early examples. Automated email routing systems, virtual help desk assistants, and scheduled reporting tools all exhibit agentic behavior.
At their simplest, these agents follow pre-defined scripts or decision trees. They recognize keywords and match them to pre-written responses which is useful for handling high volumes of routine inquiries. But these agents are limited in their ability to reason over new information, adapt to unexpected inputs, or coordinate across systems.
But agents can do so much more. Rather than following scripts, agents can reason over goals, making judgment calls about what to do next based on context, available tools, and organizational policies.
What makes an agent “agentic”
AI agents combine four key components that distinguish them from simpler automation tools:
- Memory: Agents retain context within a session (short-term) and, in durable configurations, across sessions and long-running processes (long-term). This means an agent can remember where a case left off without requiring staff to start over every time they return to it.
- Tools: Agents can use external tools such as search engines, databases, APIs, forms, and calculation engines to retrieve and act on real information rather than generating content from training data alone.
- Reasoning: Using large language models as their reasoning engine, agents can interpret complex instructions, evaluate multiple options, break problems into steps, and adapt when circumstances change.
- Autonomy: Agents can operate without a human prompting each individual step. They execute sequences of tasks and check in at defined decision points, reducing the human effort required to move a process forward.
Agents are partners, not replacements
This point deserves emphasis: AI agents aren’t designed to replace public servants. They’re designed to extend what public servants can accomplish.
The public sector relies on human judgment for good reasons. Policy interpretation, ethical decision-making, stakeholder engagement, and accountability are fundamentally human responsibilities. Agents don’t replace these; they handle high-volume, time-consuming, and process-intensive work that surrounds them, freeing employees to focus on decisions that require experience, judgment, and public trust.
Important
Agents always operate within boundaries set by people. Public servants define what agents can access, what actions they can take, and when they must pause for human review. Humans approve agent outputs, handle exceptions, and remain accountable for outcomes. AI agents are tools in service of human judgment—not substitutes for it.
The path to an AI-operated organization
Microsoft describes the evolution of AI adoption through three phases, which together define the path toward the Frontier Firm—an organization where people and intelligent agents work together at scale. Those three phases are:
- Human with AI assistant: Public servants use generative AI tools like Microsoft 365 Copilot to work more productively. AI helps draft, summarize, and find information, but humans still manage every step of every workflow. This is where many public sector organizations are today.
- Human-led, agent-supported: Agents begin handling discrete tasks and processes autonomously, with humans directing overall work and reviewing key outputs. A procurement team, for example, might use an agent to manage intake and scoring while staff focus on vendor evaluation and contract approval. This is where forward-thinking public sector organizations are heading.
- Human-led, agent-operated: At scale, agents handle entire workflows across systems and departments, with humans setting strategy, reviewing outcomes, and managing exceptions. The organization scales its mission impact without proportionally scaling its headcount.
Tip
Public sector organizations don’t need to adopt all three phases at once. Starting with a single agent that handles one specific, well-defined task is a practical and proven first step. Most successful deployments begin small, demonstrate value, and expand.
The Frontier Firm model isn’t about removing human judgment. It’s about giving public servants the capacity to apply that judgment where it matters most.