Inspiration
Foundation models are evolving faster than the agents built on top of them.
A production agent may be carefully engineered around one model generation, only for a new model to arrive with different tool behavior, structured-output semantics, context characteristics, latency, cost, or completely new capabilities.
Today, moving an agent to a new model is still surprisingly manual:
- discover the model,
- read provider documentation,
- understand its capabilities,
- change adapters and schemas,
- rerun evaluations,
- check policy and authorization boundaries,
- compare it against the incumbent,
- deploy it cautiously,
- watch for regressions,
- repeat the process when the next model arrives.
We asked a different question:
What if the agent infrastructure could evolve with the models instead?
That became Evolving Morphosis.
Evolving Morphosis is a future-model evolution control plane for AI agents. It is designed not only for models available today, but for models and capabilities that may not exist yet.
The core idea is simple:
Models change. The workload contract should survive.
What it does
Evolving Morphosis discovers, evaluates, adapts to, and safely promotes model generations without requiring the workload agent itself to be rebuilt every time.
It uses two model-ingress paths.
Known Model Fast Lane
Models we already understand can use optimized provider/model adapters.
This keeps onboarding fast and allows Morphosis to take advantage of native provider features.
However, a known adapter is still only a starting point.
The model must still pass workload-specific qualification before promotion.
Unknown Future Model Lane
An unknown model does not need to already exist in Morphosis's source code.
It can enter through a minimal bootstrap boundary.
Its self-described capabilities are initially treated as:
UNKNOWN / UNVERIFIED
—not as truth.
Morphosis then creates an evidence-backed capability picture through bounded probes.
This lets a future model introduce a capability that the original Morphosis schema did not know existed.
For example, our demo introduces:
future.semantic_patch.v1
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