Unlocking Real Value through Agents

The buzz around AI agents is undeniable—who wouldn’t want autonomous systems handling complex tasks? With frameworks like LangGraph, AutoGPT, CrewAI, or Autogen popping up, it’s easy to get swept up in the excitement. But here’s something I’ve learned from working with several AI companies: starting with a framework often leads us down the wrong path. Recently, Anthropic and Hugging Face both noted in their blogs that you don’t always need a grand “agent framework” to build effective AI solutions. That insight resonates with our own experiences. Let us understand through a real-world example on how the frameworks evolves automatically rather than starting with a framework first approach. We are not getting into nuances of states and nodes within an agent framework, as the below solution evolution will speak for itself.

A Real-World Example: Energy Optimization

Imagine an organization aiming to use AI for operational excellence—specifically, energy optimization. Suppose they’ve built an “equipment troubleshooting” agent that flags issues across multiple sites. Sounds great, right? Well, not exactly. If you can’t act on the data it produces—say by analyzing it in detail or implementing energy-saving measures—you’ve simply shifted the bottleneck from identification to implementation. Think of it like a super-fast assembly line that dumps products into an understaffed warehouse. This is textbook Theory of Constraints by Dr. Goldratt: one bottleneck replaced by another.

The same principle applies to an “energy management advisory” agent. If it spits out brilliant recommendations but lacks the context or channel to actually drive changes, it just becomes a fancy data collector. You need a third agent—an “operations data analysis” agent—to validate and contextualize those recommendations. Only then can you see the full impact of changes and genuinely optimize energy usage.

Why Framework-First Often Fails

Many teams jump in asking, “Which agent framework should we use?” Instead, you should be asking, “What workflow do we want to enable?” That shift in perspective changes everything. In the energy optimization scenario:

1. Troubleshooting Agent identifies equipment issues.
2. Energy Management Advisory Agent takes that data plus historical insights to offer fixes.
3. Operations Data Analysis Agent validates these suggestions and predicts their impact.

It’s a symphony, not a solo performance. Crucially, humans orchestrate the entire process. When you start with a solution-first approach, frameworks evolve naturally to meet real-world demands. You control the critical decisions while automating the right tasks at the right time.

How Human-Controlled Evolution Works

Companies that follow this incremental approach often see transformative results. They might begin with a basic troubleshooting agent and then build on proven successes as user needs grow. Each new layer of functionality is purposeful, driven by actual use cases rather than an abstract framework roadmap. Complexity arises only when it’s necessary—and adds clear value.

This leads to higher user adoption (because the solution fits existing workflows), more obvious ROI (each automation step has a defined purpose), and fewer cases of “impressive but isolated” agents that never deliver end-to-end value.

Key Takeaways

• Focus on Solutions, Not Frameworks: Ask what problem you’re solving, not which tool to use.
• Think in Terms of Workflows: Agents should feed into each other, creating a cohesive process.
• Keep Humans in the Loop: Critical decisions need human oversight to ensure accountability and adaptability.
• Evolve Organically: Let the solution shape the framework, not the other way around.

Your Turn

As you consider AI agents in your organization, ask yourself:

1. Are you starting with a shiny new framework or a concrete solution?
2. How will your agents collaborate to solve real-world problems?
3. Where should humans remain firmly in control?

We would love to hear your thoughts or experiences. Have you seen teams focus on individual agents or agentic frameworks and unintentionally create new bottlenecks? How are you managing the balance between automation and human oversight in your AI projects?

About ResEt AI

At ResEt AI, we’ve built SIA—our Suite of Intelligent Agents—following these principles. We focus on enabling complete workflows, ensuring humans stay in control, and adding complexity only where it truly adds value. If that aligns with your approach, let’s connect and see how we can help optimize your operations.

The conversation around AI in industry is just beginning, and your insights could help everyone build better, more responsible solutions. Let’s keep it going!