Why I’m Done Chaining Prompts and Started Orchestrating Cognition

For the past year, prompt chaining has dominated the LLM ecosystem. LangChain, AutoGPT, Flowise, everyone seems to be solving the same problem: How do we get LLMs to do more than one thing at a time?

But the more I worked with these tools, the more frustrated I became. Chained prompts work… until they don’t. Debugging is opaque. Control is limited. Traceability is a mess. And when something breaks, good luck figuring out where or why.

So I stopped chaining prompts. I started orchestrating cognition.

Prompt chaining is linear. Cognition is not.

Prompt chaining assumes a pipeline. One step feeds the next. That’s fine for basic workflows: summarize this, then classify that. But real reasoning is more complex. It loops. It branches. It checks itself. It adapts.

That’s what cognition is: structured reasoning with memory, reflection, feedback, and control.

Orchestration means treating your AI system like a network of specialized agents with a clear role, a known input/output contract, and explicit message passing.

This is the approach I’m building with OrKa: a modular framework for explainable, observable agent orchestration.

From prompt soup to cognitive pipelines

The problem with most current stacks is that they hide reasoning inside monolithic prompts. Or worse they create brittle chains of calls with no memory of state, no fallback logic, and no ability to inspect what went wrong.

With OrKa, I define cognition like I would a microservice pipeline:

1. Agents are composable units


2. Each agent has a type: classifier, generator, validator, searcher, etc.


3. All communication is logged and observable


4. YAML defines the system (not Python spaghetti)


5. You can route messages, branch flows, retry on failure, and audit results
   It’s not just more powerful. It’s more honest. Because it makes the reasoning visible.

What this unlocks

This shift in mindset opens up workflows that were previously painful or impossible:

• Fact-checking layers with agent fallback


• Memory-driven agent routing


• Real-time explainable decisions


• Self-reflecting agents


• Orchestrations you can visualize, test, and adapt live

And it does so in a way that is modular, open, and explainable.

This isn’t just engineering. It’s philosophy.

I don’t believe AGI will emerge from a single giant model. I believe it will emerge from the coordination of smaller, structured reasoning systems. Systems with memory, roles, adaptation, and self-awareness.

Prompt chaining is a hack. Cognition orchestration is an architecture.

OrKa is open-source. You can play with it today:

SDK: https://pypi.org/project/orka-reasoning/

GitHub: https://github.com/marcosomma/orka

Video: https://www.youtube.com/watch?v=hvVc8lSoADI