AI agents are moving beyond individual productivity and into government operations.
According to Gartner, at least 80% of governments will deploy AI agents to automate routine decision-making, enhancing efficiency and service delivery by 2028.
Because of this, government leaders are challenged to determine where they can deliver value with AI agents while maintaining public trust.
Their applications across government continue to expand, from detecting tax compliance discrepancies and monitoring infrastructure to improving emergency response, identifying disease outbreaks and supporting benefits administration.
These capabilities create new opportunities but also introduce new questions about trust, governance and human responsibility. Without proper oversight, AI agents can amplify:
- Bias and inequitable outcomes.
- Operational and compliance risk.
- Misinformation and narrative distortion.
- Exposure to cybersecurity and system integrity threats.
Readiness for AI agents isn’t a technology race – even if the speed of innovation can make it seem that way. When done right, it is a journey to generate value within a trusted, well-governed environment. Let’s explore the clear, practical path to becoming confidently AI agent-ready.
What are AI agents?
AI agents are systems powered by artificial intelligence (AI) that perform complex tasks or make informed decisions with varying human involvement. They surpass traditional chatbots and large language models (LLMs) by integrating data and advanced analytics tools, making them more adaptable and capable of complex reasoning across industries.
Start with a trusted data foundation
AI agents are only as capable as the data they can access, so government organizations need to create a solid data management strategy. In the era of AI, data management and data quality are essential to ensuring trusted, ethical and bias-free outputs. Much of government data is found in unstructured formats, such as text and images, and the volume is growing.
Why start with a trusted data foundation? Because the data is feeding AI tools. If that data is outdated, inconsistent, or access-controlled in unclear ways, agents will scale those problems just as efficiently as they scale service delivery. This underscores the need for organizations to treat data management as critical infrastructure before moving to AI.
AI governance, always
Once the data is cleansed and aligned, it’s essential to have agents built on a foundation that is transparent, defensible, and aligned with organizational and governing policies.
AI governance creates a system of rules, processes and cultural frameworks that guide how AI is used and ensure it's safe, fair and reliable. Using governance to deliver Trustworthy AI fulfills a public duty that creates confident outcomes and reinforces public trust.
A collaborative governance approach built around the QUAD – oversight, operations, compliance and culture – helps organizations innovate responsibly while maintaining accountability.
- Oversight: Provides the guardrails that keep AI technologies and processes grounded in data ethics principles. It ensures AI-enabled decisions stay fair, transparent and accountable.
- Operations: Turns mission needs into practical, responsible AI solutions. Absorbs and interprets demand, then develops AI technologies that align with regulatory expectations and data ethics standards.
- Compliance: Acts as an agency’s early-warning system. It monitors and audits AI systems to surface risks before they impact the public. Provides organizational checks and balances to protect mission integrity.
- Culture: Builds a workforce and ecosystem where responsible AI becomes second nature. It encourages collaboration, knowledge-sharing and everyday behaviors that reinforce ethical, transparent and accountable use of AI across the agency.
Implementing AI agents requires more than deploying technology. It requires operational readiness that aligns people, processes and technology around a shared strategy. Accountability should extend across the organization – not reside solely within IT.
What is agentic AI?
Agentic AI refers to intelligent systems, or “agents,” that exhibit a higher level of autonomy and decision-making. These systems can make decisions, carry out tasks and learn from interactions within a given environment. Agentic AI uses multiple AI agents to achieve complex goals autonomously, combining AI, automation and human oversight to redefine how businesses operate, make decisions and interact with technology.
How do AI agents deliver reliable outcomes?
Generative AI (GenAI) serves as the reasoning and communication engine behind many AI agents, allowing them to understand intent, generate responses and support increasingly complex workflows.
On its own, however, GenAI has limitations. Hallucinations, limited transparency, security concerns and a lack of built-in accountability become far more consequential in government, where decisions can affect benefits, public safety and citizen trust.
This is where retrieval-augmented generation (RAG) becomes valuable.
Rather than relying solely on a model's internal knowledge, RAG allows AI agents to retrieve approved policies, guidance and case information before generating a response. Grounding outputs in trusted organizational knowledge improves accuracy, reduces hallucinations and gives agencies greater visibility into how decisions are made.
Many of the most compelling government use cases we’re seeing today combine analytics systems with retrieval agents. Tom Sabo, Advisory Solutions Architect, SAS
Turning this combination of AI models and decision logic into an AI agent allows it to be automated. Rather than manually executing this series of programs, it can be run automatically.
Imagine a benefits worker who has spent the entire day processing cases, only to end the day with more cases in the queue than when she started. It’s a familiar dilemma – now imagine her supported by an AI agent that can:
With these actions, an AI agent can transform the caseworker’s day from reactive to strategic. It streamlines the most time-consuming steps of case review, yet the human role remains essential. She still applies judgment when sources conflict, when the case is new, or when the situation requires extra care. The result is faster, more accurate decisions supported by trusted data, while preserving the expertise and empathy that define public service.
The leadership takeaway
The use of AI agents in government is nearly limitless and can have a profound impact to automate decisioning – for better or for worse. Whether considering AI agents to analyze policy, review cases, evaluate mission-critical data, or perform tasks, government leaders need to know:
- AI agents automate decisioning from static decision logic to dynamic workflows. It is the agent itself that determines which data, models, systems and organizational rules are relevant in a given context.
- AI agents connect to tools that define what an agent can do, what it knows and then ground agent decisions in facts while preventing hallucinations.
- AI agents require governance and transparency into which data and tools are used, as well as guardrails that define what agents can do and, especially, what they cannot do. In addition, AI agents should be deliberately designed to ask for human help. This is not a limitation but an enrichment, particularly in risky, uncertain situations.
While AI agents swiftly change government operations, the most enduring transformations will pair innovation with accountability and ground every system in data integrity. After all, automation and speed mean little – to agencies or the public – if outcomes can’t be trusted.
