What if OpenAI Agent Builder had reusable cognition? (ORCA + MCP demo)

What if OpenAI Agent Builder could do something it doesn’t natively support yet?

Reusable, auditable cognition.

I built a small ORCA-powered agent on top of OpenAI Agent Builder + MCP that can take a strategic decision request like:

“Should we launch a legal SaaS in Spain during the next 12 months?”

and instead of producing a black-box answer, it executes an explicit cognitive workflow:

prompt → routing → reusable decision skill → execution trace → confidence → report

The interesting part is not the recommendation.

The interesting part is that the reasoning becomes:

reusable traceable auditable composable

And the output stops looking like:

“here are some thoughts…”

and starts looking closer to something a real team could actually use:

• explicit recommendation

• alternatives evaluated and scored

• confidence levels

• decision quality assessment

• uncertainties and missing information

• execution diagnostics

• cognitive trace of what happened

• graceful degradation when execution fails

Under the hood, the agent delegates cognition to an ORCA skill exposed through MCP.

The surprising thing?

It is actually much more practical than I expected to expose a reusable skill via MCP and plug it into Agent Builder.

But it already feels surprisingly close to something agent platforms will eventually need.

Because once agents become business-critical:

“trust me bro, the model reasoned” probably won’t be enough.

This is part of ORCA (Open Cognitive Runtime for Agents) — an open-source framework in progress for reusable cognition in agents.

Repo: https://github.com/gfernandf/agent-skills

Paper: https://zenodo.org/records/19438943

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6600840

Curious question for people building agents:

Should cognition stay inside prompts, or should it become a reusable runtime layer?