A Nonprofit Leader’s Guide to AI Agents
By Kelly Perry
August 29, 2025
AI agents have now entered the chat. These autonomous software systems act as digital twins of their human counterparts – operating independently, without the need for intervention. This AI application offers nonprofits numerous benefits: efficiency gains, increased capacity, and greater mission impact. Given this potential, what considerations should nonprofits make as they adopt this technology? Which functions should agents perform? And, where should they begin?
In this blog post, we will address those questions and outline:
- The definition of AI agents and their benefits
- Key differences between agents and other AI tools
- Nonprofit challenges agents can address
- An example of AI Agents in action
AI Agents Defined
AI agents are autonomous software systems that perceive their environment, make decisions, and take actions to accomplish tasks without continuous human intervention. Think of an agent as a digital team member – one who is an expert in their designed function, doesn’t sleep, and performs their set of defined tasks all in the background. This allows you to devote time, energy, and intention to higher value tasks.
At the center of an AI agent is a LLM (large language model), powering an agent’s ability to observe their environment, reason, and act. Agents collect data from multiple sources including user interaction, sensor data, its own memory, internal data sources like KPIs/metrics or knowledge documents, and external data sources such as APIs and public URLs. Agents base their decision-making on their knowledge set. If they happen to lack information, they refer to other agents or external environment to find the answer. After they receive this information, it is committed to memory, and the agent will self-correct, ensuring better decision-making in the future.
This continuous cycle of real-time decision-making, context awareness, and learning distinguish agents from standalone LLMs like ChatGPT, Gemini, and Claude.
A Comparison Guide: LLMs, Chatbots, and Agents
Although related, LLMs, chatbots, and agents each serve different purposes. Let’s break down these concepts in detail.
Large Language Models (LLMs) are the models such as GPT-5, Claude, or Gemini that are trained on a massive amount of pre-fixed data. These systems handle open-ended tasks, prompted by their users and are the foundation for chatbots and AI Agents. A key misunderstanding of LLMs is that they are continuously updated with the most current data. To stay up to date, they need updates from retraining, connected tools, or external memory systems.
Chatbots use Natural Language Processing to respond to human queries. This AI application needs continuous intervention to continue and only responds using its knowledge set. They are best defined as conversational interface tools, existing as support agents or performing other specialized tasks. Ultimately, they are best designed for reactive, transactional interactions.
| AI Agent | Chatbot | LLM | |
|---|---|---|---|
| Definition | Autonomous systems that use LLMs to act | Apps designed to converse with users | Core AI models trained on fixed datasets |
| Data Source | LLMs + Memory + Tools | Static Data Provided at Setup or LLMs | Training Data |
| Knowledge Base Growth | Yes | No | Not Automatically |
| Human Input | Not Necessary Post Setup | Required | Required |
| Continuous Learning | Yes | No | Not Automatically |
| Environment Interaction | Yes | No | Depends |
Let’s explore an example highlighting these roles. A user may interact with a LLM – such as Claude – by asking “What is donor stewardship?” Claude will return an answer based on its training data.
If a user is within their CRM system, they may ask a chatbot “How can I set up an automation for donor stewardship?” The chatbot will respond with a step-by-step guide that a support agent supplied the chatbot.
Finally, a user could interact with a specialized “Donor Stewardship Agent.” The user could inquire “Who has given once this year but not again? How can I reengage with these donors?” The agent will run a query, reason over the donor records, and propose the next action.
Nonprofit Challenges Agents can Address
In today’s nonprofit environment—marked by staff shortages, rising donor acquisition costs, and mounting pressure to “do more with less”—AI agents hold tremendous promise in closing critical gaps and empowering organizations to thrive. By outsourcing routine or time-intensive tasks, such as building a sustainer landing page, agents unlock up to 40% productivity gains according to McKinsey, enabling small teams to handle much larger workloads. This not only allows staff to focus on the high-value activities that drive revenue but also makes data-driven decision-making more actionable by eliminating guesswork.
Beyond internal productivity, agents also address external pressures: delivering personalization at scale to meet donor expectations, strengthening retention by speaking directly to supporter interests, and ultimately creating more funding capacity to test new acquisition channels. The result is a healthier balance—teams that are more effective, donors who feel deeply engaged, and organizations better equipped to navigate today’s challenges with agility.
Example of AI Agents in Action
AI agents can be built to perform a variety of functions for nonprofit organizations from productivity-related tasks, content creation, donor management, and finance. Let’s explore a specific example. Imagine your organization is a regional food bank preparing for a fall campaign, with a goal of upgrading mid-level donors—those giving between $250 and just under $1,000 annually—into recurring supporters or candidates for major gift cultivation. AI agents can help make this possible at scale without adding staff.
A CRM and Fundraising Agent can analyze giving frequency, recency, and amounts to spot donors who have steadily increased their gifts or engaged consistently, flagging them as high-retention, high-potential prospects for upgrade.
A Community and Digital Engagement Agent can look at interactions such as volunteering, event attendance, or email engagement to identify those with strong interest, enabling outreach tied directly to their passions—for instance, connecting a highly engaged supporter to a school lunch program campaign.
Meanwhile, a Matching Gift Agent can surface donors who haven’t yet claimed their employer’s match, unlocking additional revenue potential and even signaling prospects with profiles suited for major giving. Together, these agents equip your organization to pinpoint the right donors, personalize outreach, and move them toward deeper commitments—all while freeing up staff to focus on relationship building rather than manual analysis.
Where to Begin with AI Agents
With all this power at your fingertips, it can be difficult to know where to begin with AI agents. Start by considering one challenge your organization needs to solve. Perhaps your team lacks internal resources to develop content or digital assets. Or you may need assistance in identifying hidden major donors in your database and launching personalized pipeline development. These challenges can be solved for using an AI agent. Start with one use case, explore your current tool sets that leverage pre-built agents (such as StratusLIVE), and build in stop gaps that allow for user intervention. What’s important is that you familiarize yourself with how your agents are trained and remember that they are only as useful as the data they can access.
AI agents are no longer abstract concepts—they’re practical tools that can help nonprofits overcome resource constraints, deepen donor relationships, and deliver personalized experiences at scale. By starting small with a clear use case, leveraging agents within existing systems, and maintaining thoughtful human oversight, organizations can unlock the benefits of autonomy and efficiency without sacrificing mission alignment. As the nonprofit sector continues to navigate rising costs, shifting donor expectations, and the pressure to do more with less, AI agents offer a path toward greater resilience, smarter decision-making, and ultimately, stronger impact.
To learn about AI agents and to view the StratusLIVE Web Designer Agent in action, check out our on-demand webinar.
