AI Agents vs. Agentic AI: What Nonprofits Really Need to Know
AI is changing how we work and what we expect from technology.
Generative AI opened the door to learning and creativity. Now, agentic AI is stepping in to do something even bigger: help us get work done.
With tools that can execute tasks and manage workflows, the latest leap in AI innovation is redefining productivity and decision-making for every role.
But as technology evolves, so does the language we use to describe it, making it hard to keep up with new terms and concepts.
If you’re feeling a bit overwhelmed, you’re not alone.
This guide will break down what differentiates agentic AI from other forms of AI agents and explore practical examples. We’ll also share insights to help you make informed decisions, so you can feel confident and energized as you lead your organization forward.
What are AI agents vs agentic AI?
The terms “AI assistants,” “AI agents,” and “agentic AI” are often used interchangeably, but they describe different levels of capability.
In fact, there’s a fair amount of grey area between these concepts, and the lines are continually evolving as technology advances.
AI agents
The term “agents” often refers to task-specific, rule-based systems designed to automate repetitive processes. They typically operate within set boundaries, following step-by-step instructions to deliver consistent results.
But not all agents are created equal. Some may incorporate limited adaptability or decision-making, making it difficult to draw a strict line between “simple” agents and more advanced systems.
Agentic AI
The term “agentic AI” represents a newer, more advanced approach. These systems are designed to act with greater autonomy. They can plan, adapt, and execute across platforms, often learning from context and making real-time decisions to pursue long-term objectives.
Once trained, agentic AI can feel more like a virtual coworker that can take initiative and drive outcomes, rather than simple automation.
In practice, the line between AI agents and agentic AI is often blurry. Different tools and platforms may combine elements from both.
Rather than thinking in terms of “either or,” it’s more accurate to view these systems on a continuum that goes from simple, rule-based automation to highly adaptive, autonomous intelligence.
Understanding this spectrum can help you better evaluate solutions and choose the right approach for your organization’s needs.
Key Aspects of AI Agents vs. Agentic AI
To help you better understand the key aspects of agents and agentic AI, here’s a summary of how this technology compares.
Keep in mind, though, that these are generalizations, and many real-world solutions combine features from both sides, so the boundaries are often fluid.
| Aspects | Agents | Agentic AI |
| Autonomy | Operate mostly within predefined rules, often requiring humans to launch subsequent steps or actions | Act more independently with goal-driven behavior (after being activated by a human), taking steps to achieve a pre-defined outcome and tracking behavior for humans to review |
| Decision-Making | Primarily follow conditional logic and workflows | Make more contextual decisions and adapt strategies |
| Adaptability | Typically require manual updates or retraining | Can learn and evolve in real time based on feedback and outcomes |
| Goal Orientation | Focus on executing specific tasks | Pursue broader, long-term objectives across multiple domains or systems |
| System Integration | Often function in isolated environments | Orchestrate actions across systems and platforms |
| Complexity | Generally simple, task-specific | More complex, capable of multi-step reasoning and planning |
These comparisons give you a sense of how AI technology can vary, but you’ll often find solutions that mix and match aspects from both sides.
Real-World Application Examples
The true value of AI comes to life in how it supports your day-to-day work.
Below, we highlight examples of how each approach can show up in the field. Some examples lean toward simple automation, while others showcase more adaptive intelligence.
Examples in Action
Customer or Donor Support: Many chatbots start as simple rule-based agents, handling routine inquiries from donors, volunteers, or beneficiaries. More advanced versions can learn from interactions and adapt responses, blurring the line between agent and agentic AI.
Automated Reminders: AI can send timely notifications for upcoming events, deadlines, or donation opportunities, ensuring no one misses a critical moment.
Donor Stewardship Automation: Some platforms combine automated workflows with adaptive, AI-driven personalization to support donor relationship building. This kind of assistant illustrates how AI can blend straightforward task automation with more context-aware guidance.
Grant Lifecycle Management: AI can proactively track grant deadlines, requirements, and reporting needs, anticipating next steps and automating communications to keep teams on track.
Personalized Campaign Execution: Advanced AI can orchestrate multi-channel fundraising campaigns, adjusting tactics in real time based on performance data and supporter feedback, helping organizations reach the right people with the right message at the right time.
These examples show that AI solutions may not fit into just one category. Instead, they often blend automation and adaptability, giving organizations the flexibility they need to achieve meaningful outcomes.
How to Choose the Right Approach to AI
Selecting the right AI approach begins with clarity about your organization’s goals and your readiness to embrace change.
Start by defining the outcomes you want to drive, whether that’s deepening donor engagement, scaling personalized outreach, or streamlining operations.
Then, assess your organization’s readiness for more advanced AI: Do you have the technical infrastructure to support agentic AI? Are your risk management practices robust enough for greater autonomy? Is your team ready and willing to invest in learning new skills and adapting to new ways of working?
By finding the right balance between your organization’s strategic goals and its readiness to evolve, you can choose AI solutions that meet your current needs while positioning you for future growth.
Key Ethical Considerations of Agentic AI
As your organization moves from AI assistants or agents to more autonomous agentic AI systems, ethical and governance responsibilities become even more important. Here’s what to keep in mind.
Human Oversight: Ensure your team retains control over decisions, especially in sensitive areas like donor stewardship or resource allocation. Build in review and approval processes for AI-driven actions.
Data Integrity: Use high-quality, representative data to train and operate your AI systems. Regularly audit data sources and ensure your technology reflects your organization’s values and priorities.
Equity and Inclusion: Evaluate your systems for fairness and inclusivity. Monitor outputs for bias and ensure your AI serves all communities equitably.
Privacy and Security: Protect sensitive information and comply with data protection regulations. Confirm that your organization retains control over how data is used and stored.
Transparency and Accountability: Choose solutions that offer clear explanations for recommendations and actions. Document how AI is integrated into your workflows and communicate openly with stakeholders.
By proactively addressing these considerations, your organization can use the power of AI agents and agentic AI to drive positive outcomes, while safeguarding trust, equity, and mission alignment every step of the way.
At Blackbaud, we’ve built our Intelligence for Good® comittment around these responsible AI principles which are embedded into every element of how we design, govern, and deploy our AI solutions.
Agents for Good™: The Future of AI for Social Impact
As you’ve seen, understanding the difference (and overlap) between AI agents and agentic AI is key to building a technology strategy that truly supports your mission.
Recognizing that these categories are not mutually exclusive, and that many solutions exist in the grey area between them will help you make more informed, flexible decisions.
Blackbaud AI delivers more for your mission. Our Agents for Good™ are purpose-built to unite the strengths of AI agents and agentic AI, acting as proactive virtual teammates that help you scale impact, personalize engagement, and operate with confidence.
With these tools, you can unlock new possibilities for growth, connection, and mission alignment, so you can focus on creating the change you want to see in the world.
Ready to accelerate your mission with the next generation of AI?
Discover how Blackbaud AI and Agents for Good™ can help your organization achieve more.
