Reasoning → Capability
Split model intent from provider choice. The model proposes a need; CLR binds it to a capability and picks a supplier with evidence.
Reasoning-to-Capability Alignment
The decision layer for AI agents. Evidence-based selection on latency, uptime, and success — not semantic vibes.
The reliability gap
As agents gain more tools, semantic noise rises. Models pick the wrong provider — or a dead one — and production accuracy collapses.
More capabilities ≠ more reliability — unless selection leaves the model’s guesswork.
The decision layer
We’re not another phone book. CLR sits above your pipes and picks the best live provider at runtime — from real vitals, not marketing text.
Lists what exists. The model still guesses which tool to call.
Ranks live candidates by latency, uptime, success — then routes deterministically.
Core capabilities
Separate intent from routing. Measure what matters. Enforce policy. Keep identity stable.
Split model intent from provider choice. The model proposes a need; CLR binds it to a capability and picks a supplier with evidence.
Live latency, uptime, TTFT, and ITL. Kill silent failures before the agent wastes a turn on a dead endpoint.
Probabilistic control that favors providers with proven marginal contribution — not the loudest description.
−28% tokens · −19% time-to-success*
Topology-independent identity so agents can move across clouds without breaking the audit trail.
Enterprise control plane
Block high-latency tools. Keep data inside the perimeter. Explain every selection — why this provider won.
# Runtime enforcement — evaluated before every invoke
select:
min_uptime: 0.99
max_p95_ms: 200
min_success_rate: 0.95
deny:
- egress:external # no data leaving the org
- write:* when phase:plan # no writes in planning
audit: explain_every_route
Early access to the CLR SDK — register a capability, publish heartbeats, and route on evidence.