After completing a strategic plan, economic development teams are faced with turning vision into action, often with limited staff and resources. Generative artificial intelligence (AI) represents a potential solution to this problem, but it can be hard for practitioners to transform the buzz around AI into practical results. To this end, AI agents can offer a path forward. They’re ready-made helpers that can take on routine tasks so staff can focus on the larger goals. At TIP, we’ve been exploring how these emerging tools might support the implementation phase—not as a solution, but as part of a broader innovation in how economic development work gets done.
So What Are AI Agents?
In a nutshell, AI agents can be thought of as a smart, specialized program that is self-learning. AI agents are designed to have their own personality and memory, coupled with access to data, tools, and an AI model. This combination allows them to pursue complex tasks. Multiple agents can share memory, coordinate actions, and delegate tasks, mimicking a well-orchestrated team. This collaborative nature enables the execution of larger, more dynamic tasks than what traditional AI systems handle and presents an opportunity for more meaningful AI integration. Here are examples of what they can do.
- Plan a step-by-step approach to solve a problem.
- Gather information from news sites, public filings, or even social media.
- Pause to think, weighing the best next move.
- Work together, delegating tasks to the right agent.
- Write content like emails, brochures, or basic reports.
- Schedule meetings, send emails, or post updates automatically, if allowed by the user.
Unlike typical chatbots such as ChatGPT, Claude, or Gemini that are reactive and respond to one query at a time, AI agents are more autonomous, capable of handling complex workflows, and are continuously learning from experience. Platforms like CrewAI and Langchain let you launch and manage several agents without writing code. You point and click, and they spring into action.
How They Could Help in Economic Development
Many agencies already use analytic models to spot growing companies or guide jobseekers. AI agents add speed and scale to those efforts. The following are four practical applications for economic development.
Business Intelligence
- Monitor early warning signs. Pull in news headlines, social posts, or corporate filings to flag layoffs, supply issues, or leadership changes.
- Analyze feedback. Save time by scanning surveys or business visit notes to find common themes, workforce gaps, or permitting hurdles.
Marketing and Outreach
- Create customized content. Draft brochures for tech firms, manufacturers, life-science companies, or other businesses with specialized industry knowledge, or translate materials into Spanish, Mandarin, or other local languages, cutting down the time spent on initial drafts and translations.
- Support grant and proposal writing. Build first drafts of proposals or grant applications and gather supporting data in minutes, helping the organization’s productivity.[1]
Operations and Productivity
- Prep for meetings. Summarize agendas and suggest talking points so teams walk in ready.
- Streamline hiring. Draft job descriptions and scan resumes to highlight top candidates when there are hundreds of applicants.
Monitoring Strategic Plans
- Track local business activity. Spot openings, closures, or expansions from newsfeeds across multiple geographies.
- Gauge public sentiment. Use simple sentiment analysis to synthesize comments or reviews.
These ongoing insights enable leaders to quickly adapt and implement strategic plans as conditions evolve.
How an AI Agent Workflow Might Look
Imagine your regional economic development board is expanding its team, and you need to hire specialists in investment attraction and workforce development. A team of AI agents like the following could streamline this process.
- Strategy agent. Defines the task, “Hire a professional with expertise in foreign direct investment and local labor trends, who is fluent in Spanish and would be a strong fit for the organization, through a process of research, drafting, and screening.”
- Research agent. Gathers similar job descriptions on the internet. Identifies emerging skills in the industry and analyzes the organization’s mission and goals. Prepares a summary of insights.
- Drafting agent. Uses the research summary to draft a tailored job description optimized for relevant job boards. Prior to posting, the description is subject to human oversight and approval
- Screening agent. Aggregates resumes submitted. Checks resumes for alignment with the job’s core requirements and ranks top candidates based on the organization’s fit and qualifications.
- Summarization agent. Prepares short profiles for the top candidates and summarizes strengths and gaps, making the field of applicants easy to review for the hiring manager.
Each agent hands off its work to the next, and a project dashboard tracks progress and logs each step. A person then reviews the final email, refines it for any nuances the AI tool may miss, and sends it. This approach lets humans focus on defining tasks that match organizational needs and making judgment calls while routine work is delegated, freeing time for meetings, strategy, and other important activities.
Challenges & Considerations
Even as AI agents show promise, their use comes with important considerations. Thoughtful implementation is key to avoiding pitfalls and ensuring tools align with real-world needs.
- Accountability and oversight. Agents can suggest actions, but people must review all outputs and own final decisions.
- Scope creep. Without clear task limits, agents may try tasks they weren’t built for. Regular log reviews keep them on track.
- Human factors. Politics, trust, and local nuance still need real conversations. Agents help, but they don’t replace personal outreach.
- Responsible use of data. The user must recognize when data is too sensitive to feed into AI models and draw a line (e.g., health data, sensitive financial data, etc.). Obtaining consent from the data owners before processing their information with AI is considered a good practice.
- Grant and proposal ethics. While AI can increase productivity in drafting grant applications and proposals, organizations must remain transparent with stakeholders about their use of AI, verify that all data sources are trustworthy and accurate, and avoid any form of misrepresentation.
Conclusion & Practitioner Recommendations
AI agents are an innovation that can be a force multiplier for small teams working toward big goals. Start with a narrow pilot—maybe an agent that tracks business headlines or creates the first draft of your next grant application. Assign ownership and review results. Then expand to new tasks. With clear oversight and careful planning, AI agents can become trusted assistants in driving local prosperity.
We want to hear from you!
How are you using AI? Please take a moment to share your thoughts.
[1] In 2023, the Grant Professionals Association released the “Code of Ethics and Artificial Intelligence Tools Using Large Language Models [LLM],” which presented examples of AI/LLM applications along with an approved statement on their use. See https://grantprofessionals.org/page/aiandgrants for more on applications and guidelines.
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