Daniel Chenok

Contributors for this blog are Bridget Deary, Partner, IBM Consulting Federal, Mark Fisk, Partner, IBM Consulting Federal, Chuck Royal, Associate Partner, IBM Consulting Federal, and Tony Vachino, Associate Partner, IBM Consulting Federal.

Accelerating Mission Outcomes with Agentic AI: A Practical Guide for Federal Leaders

Agentic AI is rapidly reshaping the way organizations operate, promising a new era of autonomy, adaptability, and mission impact. But what exactly is Agentic AI, and how can federal agencies harness its power to overcome longstanding challenges and deliver real results?

What Is Agentic AI? The Next Leap in Autonomy

Agentic AI is more than just another buzzword in artificial intelligence. Unlike traditional AI, which follows predefined rules or generates content on command, Agentic AI systems act as independent agents: they perceive their environment, set goals, make decisions, and take actions—all with minimal human intervention. Think of Agentic AI as a trusted digital teammate that not only understands your objectives but proactively finds the best path to achieve them, learns from every interaction, and adapts in real time to changing circumstances.

These systems are built from networks of AI agents, each specializing in specific tasks, collaborating to solve complex, multi-step problems. The result? Workflows that are more efficient, flexible, and resilient—freeing up human talent for higher-value missions.

Why Federal Agencies Need Agentic AI Now

Federal agencies, are under unprecedented pressure to modernize, innovate, and do more with less. Executive Orders and agency mandates now require outcome-focused innovation, rapid adoption of AI, and measurable impact on mission delivery. Agentic AI is uniquely positioned to meet these demands by:

• Automating complex, cross-agency workflows
• Enabling real-time decision-making
• Scaling expertise and insights across the enterprise
• Improving data-driven outcomes while maintaining compliance and transparency

Four Core Challenges on the Path to Agentic AI

While the vision is clear, the journey is not without obstacles. Federal agencies face four main hurdles:

• Data Integration Without Ownership: Much of the data needed for effective AI is scattered across agencies, partners, and external sources. Integrating this data securely and efficiently—despite privacy, proprietary, or interoperability issues—is a top challenge.
• Ensuring Data Quality and Trust: AI is only as good as the data it consumes. Inconsistent metadata, unstructured information, and siloed systems can undermine trust and performance.
• Legacy Systems and Automation: Outdated infrastructure and fragmented automation limit the ability to deploy AI at scale. Modernizing incrementally, without disrupting mission-critical operations, is essential.
• Integrating Commercial Tools with Modular Architecture: The commercial AI sector is innovating at a breakneck pace, but federal IT systems often lack the modular, API-driven architecture needed to plug in new tools quickly and flexibly.

How Agentic AI Overcomes These Barriers

Agentic AI frameworks are designed to address these challenges head-on:

• Seamless Data Integration: By leveraging ontologies and knowledge graphs, Agentic AI can connect and contextualize data from diverse sources, making it discoverable, trustworthy, and actionable.
• Real-Time Adaptability: Agentic systems learn from every interaction, continuously improving their decision-making and adapting to new data and mission needs.
• Legacy-Friendly Modernization: Agentic AI can layer on top of existing systems, maximizing the value of prior investments while incrementally introducing new capabilities.
• Modular, Open Architectures: Modern Agentic AI solutions are built to be modular and standards-based, allowing agencies to integrate best-in-class commercial tools without vendor lock-in.

Real-World Impact: Agentic AI in Action

Agentic AI is already delivering value across government and industry:

• Automated HR and Procurement: AI agents handle millions of HR queries and transactions, freeing up staff for higher-value work and accelerating procurement cycles.
• Mission-Critical Operations: In defense, Agentic AI platforms are integrating data from sensors, documents, and external sources to provide real-time intelligence and streamline mission planning.
• Accelerated Compliance and Innovation: Agentic AI helps agencies meet evolving federal mandates for transparency, auditability, and risk management—while enabling rapid, compliant innovation.

Getting Started: A Practical Roadmap

Federal leaders looking to unlock the potential of Agentic AI should focus on:

• Prioritizing High-Value Use Cases: Start with processes that offer the greatest potential for impact and scale.
• Mapping Current Systems and Data: Assess where your data lives, how it’s structured, and where integration gaps exist.
• Building Modular, Open Architectures: Invest in flexible IT frameworks that can evolve as new AI tools emerge.
• Fostering Cross-Functional Teams: Bring together mission owners, data experts, and technologists to ensure solutions are aligned with real-world needs.
• Emphasizing Security and Compliance: Ensure that every step of your AI journey meets federal standards for transparency, auditability, and risk management.

The Bottom Line

Agentic AI is not just the next step in automation—it’s a fundamental shift in how agencies can achieve their missions. By enabling systems to act, learn, and adapt, Agentic AI empowers federal leaders to break through long-standing barriers, deliver measurable outcomes, and build a future-ready government.

This article first appeared here: Accelerating Mission Outcomes with Agentic AI: A Practical Guide for Federal Leaders | IBM Center for The Business of Government.

We expect the following reports to be published starting in late 2025.  Short summaries of each report follow:

Accelerating Digital Transformation in Public Organizations Through Public Procurement by Ana Maria Dimand, Florida State University; Petra Ferk, New University, Slovenia; Jane Lynch, Centre of Public Value Procurement, Cardiff Business School, Cardiff University, UK; Andrea Patrucco, College of Business Florida International University; Esmee Peters, University of Twente, Enschede, Netherlands.

This report will investigate the impact of emerging technologies on public procurement processes across various countries. It will analyze how different nations implement these technologies, identify challenges in adoption, and assess their effects on efficiency, transparency, and cost-effectiveness. The report will provide actionable insights for public sector leaders in the U.S. and globally as well as a comparative analysis of technology implementation, best practices, common challenges, and policy recommendations.

Combining AI and Metadata Analytics for Enhanced Government Financial Oversight by Dr Irakli Petriashvili, Rutgers School of Public Administration’s Transparency and Governance Center

This report will investigate how AI and metadata analytics can enhance financial auditing processes at the federal level to detect fraud. By evaluating current audit practices of the GAO and agencies, analyzing public spending metadata, and applying machine learning models, the study will identify inefficiencies in traditional approaches and propose innovative solutions. Based on real-world datasets, the research aims to leverage government spending data in developing recommended strategies to increase financial audit capabilities. The findings aim to improve early fraud detection and enhance public sector performance and accountability. 

Harnessing Generative AI: Boston’s Institutional Setup and Approach to Data-Driven Governance and Civic Engagement by Juncheng Yang, Data-Smart City Solutions, Harvard Bloomberg Center for Cities. 

This report will explore Boston’s integration of Generative AI (GenAI) in urban governance, focusing on its pilot adoption and institutional foundation. By analyzing Boston’s position as the first U.S. city to release GenAI guidelines and its history of technological innovation, the study examines the institutional drivers and challenges in launching and governing recent GenAI initiatives. The investigation highlights GenAI’s impacts on data-driven decision-making, transparency, accountability, and inclusive civic engagement. The final report will offer evidence-based strategies, best practices, and actionable, scalable recommendations for adoption by local, State and Federal government leaders. 

Immersive Workspaces: Reimagining the Future of Governmental Work for Innovation, Efficiency, and Wellbeing by Alexander Richter, Victoria University of Wellington and Shahper Richter, University of Auckland.

This report will examine and illustrate how immersive workspaces, leveraging technologies such as AI, VR, AR, and digital twins, can transform government workforce practices. The report will analyze established methods based on discussion with government officials in several countries, and will apply future-thinking approaches to explore the potential for workforce transformation using these technologies. The resulting report will combine forward-thinking scenarios of the future of government with actionable recommendations to help government agencies build adaptive & resilient teams, drive efficiency, and secure mission alignment.   

Improving Federal Government Performance by Dr Howard Risher and James Perry, Indiana University. 

This report will develop a practical strategy for improving agency performance, based on ‘hands-on’ involvement in the management of organizations across agencies and organizational levels with experienced officials and experts in private and public sector management.  The report will highlight proven national and international ‘best practices’ that will be valuable to government leaders.