In 2025, Enterprise Leaders Should Grasp the Ins and Outs of AI Agents
2025 marks the escalation of AI agents in the limelight. Aren't we encoreing this prediction from 2023, you ask? Back then, I had discussed the concept of chaining AI models during one of my podcasts. The enthusiasm surrounding AI was palpable, and by 2024, numerous corporations were eager to delve into generative AI, some employing chained models. However, a significant chunk of these projects encountered scaling issues. The primary reasons? Lack of system integration or missing guardrails and quality controls. Nevertheless, the issues remain product-centric in 2025.
To comprehend the capabilities of AI agents in 2025, let's dissect a simple illustration - an automatic reply tool for emails. Picture a system that autonomously composes email replies. This example sheds light on the prospects and obstacles businesses encounter with AI agents.
GPT Wrappers, Not Necessarily Enterprise AI
Constructing an automatic reply tool is relatively straightforward with GPT wrappers. I noticed an influx of these in 2024. It's essentially setting up the AI to engage in a basic interface. In our case, this involves acquisition of a ChatGPT API key, coding the email as input, adding a prompt, and exhibiting the response in an intuitive user interface.
Despite its simplicity, potential hurdles for enterprise AI emerge in this illustration:
- Lack of System Integration: The tool lacks the ability to access the calendar for checking availability.
- Absence of Context: It fails to comprehend my sentiments towards the individual or the event.
- Inadequate Security: What if the email solicits private information, like my Social Security number?
- Lack of Guardrails: How would it address a contentious question, such as eliciting political views?
- Lack of User Control: How much influence does the user have in shaping the AI's response?
- Oversight: AI occasionally presents inaccuracies, as the ChatGPT disclaimer constantly reminds us: “ChatGPT may err.”
Large language models excel in tasks such as summarization or functioning as interfaces, but they are not sufficient on their own. As I emphasize in my eCornell certificate program, each of these challenges can be resolved, sometimes by building AI agents within a workflow framework.
The Framework of AI Agents: Going Beyond Generative AI
AI models can be interconnected or "chained" to create workflows where the output from one model serves as input for the next. Envision tools like Zapier or IFTTT but propelled by AI. Instead of fixed steps, the process is dynamic and adapts to each situation. These workflows don't always rely on generative AI like ChatGPT. In fact, they often avoid generative AI due to its slowness and costliness.
This is how it might work for our email tool:
- Input: Hi Lutz, you are invited for dinner on Thursday. Can you come? Cheers, Tim.
- System Prompt: Evaluate the email and determine the essential steps to respond.
- System Output: (1) Verify schedule availability. (2) Review previous emails with Tim and past dinner invites. (3) Estimate the likelihood of attendance based on past actions. (4) Compile three potential replies: one accepting, one declining, and one seeking more information.
- System Execution: Execute the prescribed steps.
- Output: Present the options to the user.
This is how a chained AI model functions. It surmounts several of the earlier issues. Nonetheless, the large language model is simply one component.
All components must be seamlessly integrated and verified. For instance, our setup encompasses:
- System Integration: Accessing calendars and pulling data is not AI but requires system connections.
- Context Search: Utilizing AI to retrieve pertinent past interactions (known as RAG or Retrieval-Augmented Generation).
- Classic AI: Predicting likelihood of attendance involves conventional data analysis.
- User Design: Offering multiple options heightens usability and user input.
In essence, it's about product development - creating dependable and valuable solutions.
2025: AI Agents for Corporations
2025 signifies the ascent of AI agents. AI will streamline, boost, or automate workflows across industries, yet there will be no "silver bullet." If such a tool exists, it might likely be Search, as my prediction points out. Alternatively, we'll witness smaller workflows in areas like client care (refer to my investment in ultimate.ai), legal assistance (see flank.io), or sales (check out r2decide.com).
To construct these solutions, engineerings and product managers must focus on creating value. In my course, "Designing and Building AI Solutions," I impart a pragmatic framework for delivering value to business clients in media, finance, health care, eCommerce, and other industries:
- Establish the business objective.
- Retrieve and clean the data.
- Develop the agentic workflow.
- Test with clients.
- Establish a feedback loop.
This sounds elementary, but there are numerous considerations, including dealing with challenges such as bias and ethical issues. Most importantly, the focus must remain on value creation. Let's collaborate.
Check out Our Website or connect with me on LinkedIn for additional 2025 AI forecasts from me.
In 2025, the integration of AI agents in corporations is crucial for streamlining and automating workflows. However, this process requires more than just using GPT wrappers; it requires system integration to access calendars and pull data, context search for retrieving past interactions, and classic AI for predicting outcomes. Additionally, user design is essential to offer multiple response options, ensuring usability and user input.
Overcoming the challenges of AI agents in 2025 necessitates chaining AI models to create dynamic workflows. These workflows can be propelled by AI, like Zapier or IFTTT, and they don't always rely on generative AI due to its slowness and costliness. The process involves evaluating the email, verifying schedule availability, reviewing past interactions, estimating attendance likelihood, and compiling multiple response options.
To construct reliable and valuable AI solutions in 2025, engineers and product managers must focus on value creation, following a pragmatic framework that includes establishing the business objective, retrieving and cleaning the data, developing the agentic workflow, testing with clients, and establishing a feedback loop. This framework will help businesses address bias and ethical issues in a value-driven manner, collaborating towards a successful AI integration.
