[{"data":1,"prerenderedAt":1108},["ShallowReactive",2],{"/en-us/the-source/ai/ai-agents-for-government-a-4-step-guide/":3,"footer-en-us":51,"the-source-banner-en-us":358,"the-source-navigation-en-us":370,"the-source-newsletter-en-us":398,"article-site-categories-en-us":409,"ai-agents-for-government-a-4-step-guide-article-hero-category-en-us":411,"ai-agents-for-government-a-4-step-guide-the-source-source-cta-en-us":435,"ai-agents-for-government-a-4-step-guide-category-en-us":449,"ai-agents-for-government-a-4-step-guide-the-source-resources-en-us":461,"ai-agents-for-government-a-4-step-guide-article-hero-author-en-us":500},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"slug":8,"type":9,"category":5,"config":10,"seo":15,"content":20,"_id":44,"_type":45,"title":46,"_source":47,"_file":48,"_stem":49,"_extension":50},"/en-us/the-source/ai/ai-agents-for-government-a-4-step-guide","ai",false,"","ai-agents-for-government-a-4-step-guide","article",{"layout":11,"template":12,"featured":6,"author":13,"sourceCTA":14},"the-source","TheSourceArticle","bob-stevens","source-lp-ai-powered-efficiency-modernizing-government-in-2025",{"config":16,"ogImage":17,"title":18,"description":19},{"noIndex":6},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1759170189/zmuyphijace09hvotjtp.png","AI agents for government: A 4-step guide","Transform federal software development with AI agents that accelerate delivery, reduce costs, and modernize legacy systems for mission success.",{"title":18,"description":19,"date":21,"timeToRead":22,"heroImage":17,"keyTakeaways":23,"articleBody":27,"faq":28},"2025-09-30","5 min read",[24,25,26],"AI agents automate complex development tasks, allowing federal teams to focus on strategic, mission-critical initiatives.","Government agencies can modernize legacy systems and reduce technical debt through intelligent AI-powered automation.","Success requires a phased approach: build AI comfort, establish governance, deploy agents, and continuously improve processes.","What if government agencies could cut software development timelines in half while simultaneously tackling the technical debt that has accumulated over decades? AI agents are making this possible, transforming how public sector organizations build and maintain the digital infrastructure that serves their citizens.\n\nRecent policy directives highlight the promise of AI for federal operations, encouraging agencies to use AI to deliver enhanced taxpayer value, improve public service quality, and boost operational performance. Software development within agencies presents an immediate opportunity for implementing AI.\n\n[AI agents](https://about.gitlab.com/the-source/ai/agentic-ai-unlocking-developer-potential-at-scale/) can drive this transformation by deploying intelligent systems that can initiate actions, make autonomous choices, and handle sophisticated tasks alongside development teams. This dramatically speeds up software creation cycles, allowing organizations to ship applications more quickly without compromising security and regulatory compliance.\n\nIn addition to efficiency gains, AI agents enable cost-effective modernization initiatives, including the reduction of technical debt, remediation of security vulnerabilities, and [the updating of legacy applications](https://about.gitlab.com/the-source/ai/how-ai-can-fix-governments-legacy-code-problem/) that federal agencies have relied on for decades. AI agents could solve problems such as decommissioning obsolete systems and outdated programming languages like COBOL.\n\nHowever, the effective use of AI agents requires a fundamental shift in development practices, moving toward true human-AI partnerships. This evolution doesn't eliminate the need for talented software engineers; rather, it enhances their abilities and reshapes their responsibilities in addressing the public sector's unique mission-focused challenges.\n\nTo capture the long-term advantages of AI agents, agencies must reimagine their development structures and workflows. This starts with a step-by-step approach to AI adoption, building core knowledge of AI agents and gradually developing into an ongoing optimization process.\n\n## Step 1: Build foundational AI proficiency\nOrganizations new to AI should start by developing familiarity, confidence, and expertise with AI-supported programming, documentation, and basic problem-solving in low-risk environments. This approach helps agencies develop effective practices while preventing issues such as data exposure and security gaps.\n\nAfter developers gain basic AI competency, they can broaden their AI proficiency throughout the software development process, focusing specifically on repetitive, labor-intensive tasks where AI delivers immediate benefits with minimal workflow disruption. This frees up developers from routine, monotonous work, enabling them to concentrate on more important challenges and strategic planning.\n\n## Step 2: Establish governance frameworks and compatibility standards\nWhen teams gain confidence using AI for specific applications, agencies can start developing comprehensive guidelines for AI tools that support long-term success. This includes data access controls, security measures, and quality benchmarks.\n\nIn the U.S., the federal government has urged agencies and federal data leaders to create standards for data compatibility, standardize data formats, and build procedures to manage security threats. For federal organizations handling sensitive civilian data, implementing these standards allows agents to function within the parameters of agency compliance, security, and mission-driven objectives.\n\nThese standards empower rather than limit. Data protocols standardize how AI systems exchange information and work together across platforms. This ensures that agencies can maintain data interoperability and consistent data formats, and leverage AI tools across departments.\n\n## Step 3: Deploy and expand AI agent capabilities strategically\nNow agencies reach the most exciting phase of their AI agent implementation: when they are ready to assign AI agents to handle self-contained development projects with a degree of autonomy.\n\nProviding autonomy increases the range of responsibilities agents can manage, expanding beyond single-agent capabilities and allowing multiple agents to work together on sophisticated projects. By operating and communicating within a unified platform that provides access to rich context across the entire software development lifecycle, agents can become an integral part of the end-to-end development process.\n\nAgencies should encourage developers to strengthen the skills that matter most for successful AI partnerships, including analytical thinking, problem-solving skills, and creativity. The collaboration between AI agents and skilled, AI-prepared developers advances innovation and enables quicker, more secure software delivery in support of federal objectives.\n\n## Step 4: Enhance performance through monitoring and training\nAgencies must keep refining their AI implementation processes, even when AI agents can function and collaborate independently. To ensure agencies maximize their AI investment, it is essential to implement monitoring systems that track agent effectiveness with clear metrics and correction protocols.\n\nContinuous training programs for developers, IT administrators, and other federal employees, particularly those emphasizing AI knowledge, will also be critical. Agencies can build AI capabilities through education initiatives that emphasize prompt design, AI partnership methods, and effective system supervision. Collaborating with AI represents a sophisticated skill that will distinguish developers and prepare them for success in today's evolving work environment.\n\n## Embracing the future of AI-led development in government\nAI agents are poised to transform the way we design, create, launch, and maintain software that powers federal government systems.\n\nThe shift toward AI-driven software development creates both major opportunities and strategic challenges for public sector organizations. Agencies that welcome this change as an opportunity to reimagine their capabilities, rather than viewing it as a threat, will experience the most significant efficiency improvements and mission acceleration.\n\nBy actively managing these essential areas, from initial integration through governance and ongoing learning, federal organizations can succeed in this new environment where AI capabilities enhance human innovation and strategic thinking.",[29,32,35,38,41],{"header":30,"content":31},"What are AI agents and how do they transform government software development?","AI agents are intelligent systems that can initiate actions, make autonomous choices, and handle sophisticated tasks alongside development teams. They dramatically speed up software creation cycles, allowing government agencies to ship applications more quickly while maintaining security and regulatory compliance. These systems enable cost-effective modernization initiatives including technical debt reduction and legacy system updates.",{"header":33,"content":34},"What is the four-step approach for implementing AI agents in government agencies?","The four steps are building foundational AI proficiency by developing familiarity with AI-supported programming in low-risk environments, establishing governance frameworks with data access controls and security measures, deploying AI agent capabilities strategically with increased autonomy for self-contained projects, and enhancing performance through monitoring systems and continuous training programs.",{"header":36,"content":37},"How do AI agents help government agencies modernize legacy systems?","AI agents enable cost-effective modernization by updating legacy applications that federal agencies have depended on for decades, reducing technical debt, remediating security vulnerabilities, and helping decommission obsolete systems. They can solve problems such as updating outdated programming languages like COBOL while maintaining mission-critical functionality.",{"header":39,"content":40},"What governance frameworks should agencies establish for AI agent deployment?","Agencies should develop comprehensive guidelines including data access controls, security measures, and quality benchmarks that support long-term success. This includes creating standards for data compatibility, standardizing data formats, and building procedures to manage security threats while ensuring agents function within agency compliance and mission-driven objectives.",{"header":42,"content":43},"How should agencies prepare developers for successful AI agent partnerships?","Agencies should encourage developers to strengthen analytical thinking, problem-solving skills, and creativity for effective AI partnerships. Continuous training programs emphasizing AI knowledge, prompt design, AI partnership methods, and effective system supervision are critical. Collaboration with AI represents a sophisticated skill that distinguishes developers in today's evolving work environment.","content:en-us:the-source:ai:ai-agents-for-government-a-4-step-guide.yml","yaml","Ai Agents For Government A 4 Step Guide","content","en-us/the-source/ai/ai-agents-for-government-a-4-step-guide.yml","en-us/the-source/ai/ai-agents-for-government-a-4-step-guide","yml",{"_path":52,"_dir":53,"_draft":6,"_partial":6,"_locale":7,"data":54,"_id":354,"_type":45,"title":355,"_source":47,"_file":356,"_stem":357,"_extension":50},"/shared/en-us/main-footer","en-us",{"text":55,"source":56,"edit":62,"contribute":67,"config":72,"items":77,"minimal":346},"Git is a trademark of Software Freedom Conservancy and our use of 'GitLab' is under license",{"text":57,"config":58},"View page source",{"href":59,"dataGaName":60,"dataGaLocation":61},"https://gitlab.com/gitlab-com/marketing/digital-experience/about-gitlab-com/","page source","footer",{"text":63,"config":64},"Edit this 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Lee has been a software architect, teacher, professor, and educator for over 25 years. 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During her tenure with Google, she was directly responsible for the reliability, performance, and efficiency of all of Google's billion-user products and infrastructure.\n\nA long-time advocate for women in technology, Farmer earned a B.S. in Computer Science at the University of New Orleans, where she established two scholarships to help level the playing field for inclusion and empowerment in technology.",{"altText":1016,"config":1023},{"src":1024},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463377/udmzbjjr5xrcrffdlphx.webp",{"componentName":520,"type":520},"sabrina-farmer","content:en-us:the-source:authors:sabrina-farmer.yml","en-us/the-source/authors/sabrina-farmer.yml","en-us/the-source/authors/sabrina-farmer",{"_path":1031,"_dir":503,"_draft":6,"_partial":6,"_locale":7,"config":1032,"seo":1033,"content":1035,"type":521,"slug":1045,"_id":1046,"_type":45,"title":1034,"_source":47,"_file":1047,"_stem":1048,"_extension":50},"/en-us/the-source/authors/sandra-gittlen",{"layout":11},{"title":1034},"Sandra Gittlen",[1036,1044],{"componentName":509,"type":509,"componentContent":1037},{"config":1038,"name":1034,"role":1040,"headshot":1041},{"gitlabHandle":1039},"sgittlen","Managing Editor, Blog",{"altText":1034,"config":1042},{"src":1043},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463466/r7ckb9h2zr4c2rsz3zlm.png",{"componentName":520,"type":520},"sandra-gittlen","content:en-us:the-source:authors:sandra-gittlen.yml","en-us/the-source/authors/sandra-gittlen.yml","en-us/the-source/authors/sandra-gittlen",{"_path":1050,"_dir":503,"_draft":6,"_partial":6,"_locale":7,"config":1051,"seo":1052,"content":1054,"type":521,"slug":1061,"_id":1062,"_type":45,"title":1053,"_source":47,"_file":1063,"_stem":1064,"_extension":50},"/en-us/the-source/authors/sharon-gaudin",{"layout":11},{"title":1053},"Sharon Gaudin",[1055,1060],{"componentName":509,"type":509,"componentContent":1056},{"name":1053,"headshot":1057},{"altText":1053,"config":1058},{"src":1059},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463475/h6j4nnvykzyfzgvl7txb.webp",{"componentName":520,"type":520},"sharon-gaudin","content:en-us:the-source:authors:sharon-gaudin.yml","en-us/the-source/authors/sharon-gaudin.yml","en-us/the-source/authors/sharon-gaudin",{"_path":1066,"_dir":503,"_draft":6,"_partial":6,"_locale":7,"config":1067,"seo":1068,"content":1070,"type":521,"slug":1082,"_id":1083,"_type":45,"title":1069,"_source":47,"_file":1084,"_stem":1085,"_extension":50},"/en-us/the-source/authors/stephen-walters",{"layout":11},{"title":1069},"Stephen Walters",[1071,1081],{"componentName":509,"type":509,"componentContent":1072},{"config":1073,"name":1069,"role":1076,"bio":1077,"headshot":1078},{"gitlabHandle":1074,"linkedInProfileUrl":1075},"swalters1","https://www.linkedin.com/in/1stephenwalters/","Field CTO, GitLab","Stephen Walters is Field CTO for GitLab. Stephen has been in the IT industry for over 30 years. He is an extensively experienced subject matter expert in Value Stream Management, DevSecOps, DevOps, ALM, SDLC and IT4IT, with management and consultancy experience across end-to-end IT disciplines. Currently also operating as an Ambassador for the DevOps Institute and an Influencer in the Value Stream Management Consortium, he is interested in all things DevOps. Stephen is a co-author of the Value Stream Reference Architectures white paper and is currently pursuing further research into Value Stream Management, Organizational Architecture and AI.",{"altText":1069,"config":1079},{"src":1080},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463392/g6ktk5qb4vcqc9wqjlf9.jpg",{"componentName":520,"type":520},"stephen-walters","content:en-us:the-source:authors:stephen-walters.yml","en-us/the-source/authors/stephen-walters.yml","en-us/the-source/authors/stephen-walters",{"_path":1087,"_dir":503,"_draft":6,"_partial":6,"_locale":7,"config":1088,"seo":1089,"content":1091,"type":521,"slug":1103,"_id":1104,"_type":45,"title":1105,"_source":47,"_file":1106,"_stem":1107,"_extension":50},"/en-us/the-source/authors/taylor-mccaslin",{"layout":11},{"title":1090},"Taylor McCaslin",[1092,1102],{"componentName":509,"type":509,"componentContent":1093},{"config":1094,"name":1090,"role":1097,"bio":1098,"headshot":1099},{"gitlabHandle":1095,"linkedInProfileUrl":1096},"tmccaslin","https://www.linkedin.com/in/taylormccaslin/","Group Manager, Product - Data Science","Taylor McCaslin is the Product Lead for AI/ML at GitLab, where he is responsible for leading the team of product managers who manage the AI Powered and ModelOps stage groups and sets the vision and direction for how to empower GitLab users to leverage data science as part of their DevOps program. Prior to joining GitLab, he held positions at Indeed, Duo Security, and WP Engine.",{"altText":1090,"config":1100},{"src":1101},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1751463427/trfasilaeasosxfcxmsm.jpg",{"componentName":520,"type":520},"taylor-mccaslin","content:en-us:the-source:authors:taylor-mccaslin.yml","Taylor Mccaslin","en-us/the-source/authors/taylor-mccaslin.yml","en-us/the-source/authors/taylor-mccaslin",1759347900058]