Hi everyone,
I’d like to share a personal case study that might be relevant to those exploring human-in-the-loop or human-centered multi-agent workflows.
Why I’m sharing this here
Most multi-agent frameworks focus on planning, automation, and task efficiency.
My experience came from the opposite direction — a deeply human one:
What happens when someone under cognitive overload uses multiple LLMs as a support team to rebuild their thinking?
This became a manual but surprisingly effective multi-LLM “Externalized Brain” system, inspired by the idea of agent collaboration.
During a period of severe overlapping stress (300+ LCU), my cognition and judgment were impaired.
So instead of relying on a single model, I began assigning roles across three LLMs:
-
ChatGPT — structuring, integration, drafting
-
Gemini — logical critique, refutation, blind-spot detection
-
Copilot — third-party tone, audience perspective, ethical framing
By copying the same question across the models, each provided a different lens.
Together, they formed a three-perspective thinking team that helped stabilize my decision-making and reduce cognitive load.
This wasn’t automated at all — it was entirely conversation-driven — but the patterns mirrored many multi-agent concepts such as role specialization, cross-checking, productive friction, and iterative refinement.
I documented the workflow, the roles of each LLM, and how this “Externalized Brain” method supported cognitive recovery:
**Case Study:**Externalized Brain: From Burnout to Recovery
Key elements covered in the write-up
-
Human-centered multi-agent collaboration
-
Conversational externalization of thinking
-
LLM role assignment and sequential reasoning
-
Using refutation and cross-model contrast to remove bias
-
Reducing cognitive load through distributed cognition
-
Connections to multi-agent research (Google, Meta, OpenAI, AWS)
Why this may interest the CrewAI community
Although my system is manual, the underlying logic overlaps with agent orchestration principles — especially for:
-
human-in-the-loop design
-
cognitive offloading with LLMs
-
multi-model role distribution
-
mental-state-adaptive workflows
If this resonates with your work or CrewAI’s direction, I’d love to discuss human-centered agent UX or how such workflows might evolve with proper automation.
Thank you for building tools that help inspire experiments like this.