AI driving innovation in government software development
The federal government is making strides in leveraging agentic AI, a new evolution of artificial intelligence, to enhance value for taxpayers, improve public services, and drive operational efficiency.
What is Agentic AI?
Agentic AI introduces intelligent systems capable of taking initiative, making independent decisions, and executing complex tasks in collaboration with developers. This latest development in AI allows developers to focus on higher-level strategic initiatives within federal agencies.
Prioritizing Security and Governance
Current efforts and best practices for implementing agentic AI in the federal government prioritize identity security, governance, scaling, and continuous improvement.
Identity Security
Agencies must design identity and access management from a principle of least privilege, granting AI agents only the data and system permissions necessary for their tasks. Controls should be data-centric, with permission boundaries aligned to data classifications and continuous validation to adapt permissions based on real-time usage.
Governance and Accountability
Federal agencies emphasize auditability and traceability to ensure transparency in agentic AI decision-making. This includes maintaining detailed logs of AI system actions, enforcing role-based access controls to prevent agents from exceeding their scopes, and aligning deployments with ethical principles such as fairness, privacy, and accountability.
Scaling and Deployment
There is an emphasis on building strong, secure, and clean data foundations, including rigorous data cataloging, normalization, privacy preservation, and FedRAMP-certified storage. Large federal agencies are aligning AI initiatives with mission goals by identifying relevant high-impact workflows for automation and agentic AI augmentation.
Continuous Improvement
Agencies are focused on improving performance through dynamic stakeholder engagement, adapting agentic AI communications based on real-time user interactions, and continuous permission right-sizing driven by actual agent behavior.
Integrating Agentic AI into the Software Development Lifecycle
Agencies are deploying AI agents to handle self-contained development tasks with a degree of autonomy. These agents can become an integral part of the end-to-end software development lifecycle, residing and interacting on a single platform. Agentic AI capabilities include automating failure remediation, code reviews, test generation, security policy enforcement, and change management.
To ensure effective use of AI agents, agencies should establish governance and interoperability standards for AI tool use, including data access permissions, security protocols, and quality standards. Ongoing education programs for developers, IT leaders, and federal workers focusing on AI literacy are crucial for continued benefits.
Modernization and Cost Savings
Beyond increased efficiency, agentic AI supports cost-saving modernization efforts, such as reducing technical debt, fixing security vulnerabilities, and refactoring legacy applications. The federal government encourages agencies and federal data leaders to establish criteria for data interoperability, standardize data formats, and develop processes to address security risks.
A Human-AI Collaborative Environment
Enabling autonomy expands the scope of tasks agents can handle, allowing for collaboration on complex projects. However, it's essential to rethink development frameworks and processes to harness the long-term benefits of agentic AI, starting with an iterative approach to AI implementation.
To ensure continued value from AI investment, agencies should implement systems that monitor agent performance with clear metrics and correction protocols. Agencies that embrace this shift as an opportunity to redefine what's possible, rather than a threat, will achieve the greatest efficiency increases and speed to mission.
Artificial intelligence agents, powered by Agentic AI, are being utilized to enhance the efficiency and value delivery of federal services, taking on complex tasks and freeing up developers to focus on higher-level strategies.
To effectively integrate AI agents into their systems and optimize their performance, federal agencies prioritize identity security, governance, scaling, and continuous improvement, among other best practices. This includes granting AI agents only the necessary data and system permissions, maintaining detailed logs of AI system actions for transparency, and continuously adjusting permissions based on real-time usage.
