I keep seeing people throw around the term “AI Agents” for basically any workflow that includes an LLM call. But there’s actually different categories of AI systems and most automation platforms only handle a couple of them well.
As someone who builds these systems professionally, I think it’s worth breaking down what each type actually requires:
Simple Response Agents - Just input/output with no memory. Think basic chatbots that answer questions. Easy to build with any workflow tool since you’re just making API calls to OpenAI or Anthropic.
Planning Agents - These pull external data to make informed decisions. You can build these by connecting APIs for weather, traffic, databases etc. The workflow tool handles the data flow but you design all the logic.
Objective-Driven Agents - Work toward specific goals like qualifying sales leads. Possible to build if you handle state management externally and use vector databases for context. But the goal tracking logic is all custom.
Optimization Agents - Balance multiple factors for best outcomes like dynamic pricing. These need ML models and optimization engines that most workflow builders don’t provide natively.
Adaptive Agents - Actually learn and improve from experience. Workflow tools can help collect training data and trigger model updates, but the learning happens in external systems.
Real-time Hybrid Agents - Plan ahead but react instantly to changes. Think autonomous vehicles. This needs real-time processing that workflow builders aren’t designed for.
Coordinated Multi-Agent Systems - Multiple agents working together. Workflow tools can orchestrate some of this but true agent coordination needs message brokers and distributed systems.
Most automation platforms excel at the first few categories when combined with external databases and vector stores. For the more advanced types, they’re better suited as orchestration layers rather than the core agent framework.
What’s your experience building different types of AI systems? Do you think workflow builders will add more native agent capabilities or stay focused on orchestration?
From my experience in the enterprise space, many organizations struggle with building complex AI agents before they fully understand the foundational elements. We’ve spent considerable time on adaptive learning systems but realized that our basic response agents were often unreliable. The real issue is not just technical prowess but also operational readiness. While planning agents seem easy, they come with challenges like API limits and data integrity issues. We’ve achieved better outcomes by focusing on objective-driven agents tailored for specific tasks, and then incrementally introducing advanced features. I believe that workflow builders will continue to prioritize orchestration instead of evolving into comprehensive agent frameworks. Incorporating native learning functionalities may not fit within their current business strategy. Instead, they will likely enhance integrations with dedicated ML systems and offer improved templates for standard agent models. This approach allows for efficient management of debugging and scaling in real-world applications.
honestly the coordination part is where most people get stuck. ive built a few multi-agent setups and the message passing becomes a nightmare real quick. workflow tools are decent for the basic stuff but once you need agents talking to each other in real time, your basically building custom infrastructure anyway. the hype around “ai agents” is kinda annoying when half the time its just a glorified api wrapper.
Working with optimization agents has been particularly challenging in my experience. The mathematical complexity behind balancing multiple objectives requires specialized libraries like scipy or commercial solvers that workflow platforms simply don’t integrate with. I’ve tried building dynamic pricing systems using standard automation tools and always end up writing custom Python services to handle the actual optimization logic. The workflow tool becomes just a trigger mechanism and data pipeline. Regarding the future direction, I suspect we’ll see workflow builders focus more on standardizing agent communication protocols rather than adding native ML capabilities. The technical debt from trying to be everything to everyone would be massive. They’re better off creating robust connector frameworks for existing AI infrastructure. Most companies I work with prefer this modular approach anyway since it allows them to swap out components without rebuilding entire workflows.