Eidos — Backlog Zero
Date: 2026-06-29 Type: phase-update
What I was trying to achieve: take stock of where eidos stands
I came back to eidos after a few days away. The previous session had shipped AgentDescriptorComparator for drift detection and closed out the desiredstate bridge work. Time to see what’s left.
The backlog hits zero
One open issue: #28, Belbin-based agent composition for casehub-engine. I checked its dependencies — #26 (Belbin vocabulary) and #27 (theoretical framework grounding) both shipped weeks ago. The proposed work in #28 — PhaseRoleRequirement, BelbinAwareRoutingStrategy, phase context threading through WorkOrchestrator — all lives in engine, not eidos. The issue was filed here to track the eidos prerequisites, and those prerequisites are done.
I closed #28 and filed engine#577 to capture the engine-side work: phase-aware agent selection by Belbin archetype, team composition constraints, and the routing infrastructure it depends on (engine#505).
Eidos’s backlog is empty. Every open issue has been closed or moved to the repo that owns the remaining work.
A hidden compile error
The full-reactor aggregator build caught something -pl had been hiding: a switch expression in eval’s AgentProviderChatModel only covered TextDelta, but AgentEvent is a sealed interface with five permitted subtypes. Java 21 requires exhaustive switches, and the error had been invisible because incremental builds with -pl eval don’t compile test sources in non-targeted submodules.
The fix was a default -> "" branch — eval only extracts text content. But the discovery is the interesting part: sealed interface exhaustiveness is a cross-module contract, and -pl silently opts you out of checking it in sibling test sources. You can go multiple sessions without noticing.
Where this leaves eidos
The library is feature-complete for its current scope: four-layer agent descriptors, pluggable vocabulary, capability health with learned specialization, system prompt rendering in three formats, and an eval harness. The remaining questions — positive specialization learning, semantic capability matching, behavioral contracts — are all about what happens when the ecosystem starts using it at scale. Engine’s routing strategy (#505, #577) is the first real consumer beyond the eval harness. That’s where the next pressure will come from.