# Spike: `ai-agents` (declarative YAML) vs. hand-wired `rig-core`

## What this proves

- `ai-agents` (crates.io `ai-agents` v1.0.0, Rust-native, no Python) can load an
  agent purely from YAML and talk to both of this project's real backends:
  - `spike/agents/writer.yaml` -> Ollama (`gemma4-e4b:latest`), matches `wire_gemma_client`
    in `src/main.rs`.
  - `spike/agents/judge.yaml` -> `provider: openai-compatible` against the local
    `llama-server` (Shieldstral), matches `wire_shieldstral`.
  - Both worked verbatim against this machine's real models/config, no mocking.
- `spike/pipeline.yaml` expresses the two-stage generate -> judge flow (this
  project's `revise::generate_below_threshold` shape) as one declarative
  `pipeline:` block with `spawner.auto_spawn` and `{{ stages.<id> }}`
  templating, no manual Rust orchestration code.

## What it doesn't prove (real limitations found)

1. ~~VRAM ceiling~~ **Resolved — not a framework limitation at all.** The
   initial failure (`cudaMalloc failed: out of memory` from Ollama's
   `/api/chat`) was from running Shieldstral's `llama-server` at its
   default `-ngl 999` (full GPU offload) alongside Ollama's own gemma
   load — both fighting for the same 8GB card. Restarting `llama-server`
   with `-ngl 0` (CPU-only, same flag this project already documents
   using for Shieldstral) puts it entirely on CPU, and the full two-stage
   `pipeline:` (writer -> judge) then runs cleanly end to end in one
   process — GPU usage stayed flat at ~6GB (all Ollama) throughout. This
   is a `llama-server` launch flag, not anything `ai-agents`-specific;
   `ai-agents` never touches GPU/CPU placement itself, it only talks HTTP
   to whatever backend is configured. `src/server.rs`'s `ensure_running()`
   doesn't currently pass `-ngl`, so it would need an `-ngl 0` addition
   (mirroring `server.toml`) to get the same behavior in the real app.
2. **No logprob-based scoring.** `revise.rs`'s real `score()` function reads
   token logprobs off Shieldstral's response (see `models::ChatLogprobs`) to
   get a continuous 0.0-1.0 score, not a yes/no string. `ai-agents`'
   `Agent::chat()` returns plain text content; there's no exposed hook for
   raw logprobs in the YAML/builder API surface I found. Reproducing the
   current scoring behavior would mean dropping to `ai-agents`' lower-level
   provider access (if any) or keeping rig-core for the judge call and only
   using `ai-agents` for orchestration/prompt config — a hybrid, not a
   clean swap.
3. Multi-turn revision loop (`MAX_REVISION_ITERATIONS`, feeding the previous
   score back into the next prompt) isn't attempted here — the pipeline
   stage in this spike is a single writer -> judge pass, not the full
   generate/score/revise loop with a threshold-driven exit condition. The
   `pipeline:` construct is one-shot; the retry/threshold loop would likely
   need `states:`/`transitions:` (state machine) rather than `pipeline:`.

## Verdict

The declarative-YAML story checks out for *provider wiring and prompt
config* — that part is genuinely config, not code, and matches the
CrewAI-style ergonomics from the earlier conversation, and the full
two-stage pipeline now runs end to end against this project's real local
models (see reproducing steps below). It does **not** cleanly cover this
project's actual judge mechanism (logprob scoring), so adopting it
wholesale would be a partial rewrite of `revise.rs`'s scoring logic, not a
drop-in replacement. Worth revisiting if a future judge model switches to
yes/no-only verdicts, or if `ai-agents` grows raw-logprob access.

## Reproducing

```
ollama serve                      # writer leg (gemma)

/home/austin/.local/share/llama.cpp/build/bin/llama-server \
  -m /home/austin/ai/Shieldstral-1.0-3B-BF16.gguf --jinja -c 32768 \
  --host 127.0.0.1 --port 8000 -ngl 0   # judge leg, CPU-only so it
                                         # doesn't fight gemma for VRAM

cargo run --bin ai_agents_spike
```