Adds a standalone binary (src/bin/ai_agents_spike.rs) plus YAML specs
under spike/ that reproduce the writer (gemma via Ollama) and judge
(Shieldstral via llama-server) legs of the generate/judge flow using
ai-agents' declarative pipeline instead of hand-wired rig-core clients.
Both legs verified working individually against real local models.
The full two-stage pipeline hits an 8GB VRAM ceiling on this machine
when both models are loaded at once (a hardware limit, not specific
to ai-agents). ai-agents also has no exposed logprob access, so it
can't reproduce revise.rs's actual scoring mechanism as-is. See
spike/NOTES.md for the full writeup and verdict.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Enable Rig's built-in tracing spans (model, token usage, cache hits,
latency) via tracing-subscriber, filterable through RUST_LOG and
defaulting to info level. Logs write to stderr so stdout stays
reserved for program output. Standardizes the remaining ad-hoc
println! diagnostics (server startup, per-iteration revision progress,
non-convergence) into structured tracing events at appropriate levels.
Checks whether llama-server is already healthy on startup and spawns it
from configured binary/model paths if not, polling until ready. Server
infra config (binary, model path, host, port, context size) split out
of prompts.toml into its own server.toml, and all of it lives in a new
server module rather than inline in main.rs, alongside a single reused
HTTP client and a shared health-check helper. Gemma client setup now
runs concurrently with the server health-check/spawn since they're
independent.
