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Evolution

Building

A run is not exhaust — it is training signal with provenance. Experiences flow back into the system, experts sharpen, and an ontology gate keeps the loop from hallucinating itself off the rails.

Most agent systems forget everything the moment a run ends. Tixim treats every run as evidence: what worked, what failed, what a human had to fix. Composed expertise plus compounding memory is the whole bet — intelligence spent once, dividends forever.

Runs leave experiences; experiences sharpen the expertsruns emitdistillrecomposerun againWorkflow runcommittees do the workExperiencepayload on Walrus, index on SuiDistillationlessons extracted, privacy-filteredSharper expertsworkflows, prompts, policiesOntology gateonly things that existintelligence is spent once, at composition time — every loop makes the next run cheaper, not just newer.
The loop: runs emit experiences, experiences distill into sharper experts, the ontology gate keeps every claim grounded in what exists.

Experiences, anchored Building

Workflow runs, service events, even manual notes can post an experience: the payload on Walrus, an append-only index entry on the flake — tagged, sequenced per epoch, readable under the flake’s own access policy. That gives downstream selection something honest to rank by: not stars, not marketing, but the recorded outcomes of real runs. The privacy filter runs before anything leaves your instance, and you decide per run whether an experience is private, project-scoped, or public.

Lessons, extracted Shipping

The knowledge subsystem already observes workflow output and extracts lessons learned: transcripts, tool calls, branch decisions and outcomes become knowledge entries that are injected into future agent context and served over MCP. Entries live a real lifecycle — active, challenged, deprecated, archived — and relevance decays with disuse, so stale advice falls out of injection instead of poisoning it. Projects can share knowledge across boundaries by explicit grant.

Distillation Horizon

The next turn of the loop: models distilling accumulated experiences into refined workflow definitions, prompts, policies — new versions of the experts themselves. Because experiences carry provenance and the results are flakes, an improved expert is a diffable, signable, attestable artifact like any other. Evolution with an audit trail, guarded behind explicit loop authorization — self-improvement is a gated operation, not an ambient behavior.

The ontology gate Horizon

A loop that feeds on its own output needs an immune system. The semantic ontology layer validates agent output against a formal model (RDF, OWL, SHACL) with three locked rules:

  1. Closed shapes — invented properties are rejected outright.
  2. Inference-aware checking — validation runs after entailment, so contradictions surface even when they are implied rather than stated.
  3. Entity grounding — every identifier must resolve against the platform’s known entities. You may only speak of things that exist. Unresolvable references are treated as hallucinations, sent back to the agent with a machine-readable report for a bounded number of repair turns, then failed.

The same discipline extends to composition: environments declare what they provide, steps declare what they require, and mismatches are rejected at submit time — before any agent runs, before any token is spent.

Together with guild certification, that closes the loop this platform exists for: a network of experts that gets better with use, and can prove it.