PROTOCOL_ID: OBSERVABILITY_CORE_V1

AI Observability & Cost Evals

AUTHOR: Peter Hanssens

2 June 2026

METRIC_ROUTING: ACTIVE

Deploying autonomous AI agents into enterprise systems introduces a critical engineering trade-off: managing token runaway costs and preventing quality decay. By employing Bifrost as a load-balancing AI Gateway and Langfuse for tracing analytics, we gain absolute visibility over our pipelines. Here is what happens when we compare refactoring with vs. without the Drover Ontology.

SCENARIO_A: WITHOUT_DROVER

Raw Ingestion

The agent runs blindly, loading all codebase contents—including dependencies and build caches—into the prompt context, resulting in compilation failures and infinite loops.

CONTEXT SIZE: 4.5 MB
HALLUCINATION RISK: CRITICAL
COMPLEX RETRIES: 12 ITERATIONS

Langfuse telemetry:

EST_COST:$210.72

BIFROST_GATE:BUDGET EXCEEDED

SCENARIO_B: WITH_DROVER

Governed Ontology

The agent utilizes local sandboxed AST symbol scans and Git Delta Ingestion Mode, reading only changed files compared to the last committed state.

CONTEXT SIZE: 61 KB (99% REDUCTION)
SANDBOX CONTAINMENT: YAEGI VM
LOCAL VERIFICATION: DroverFsck

Langfuse telemetry:

EST_COST:$0.46

BIFROST_GATE:SUCCESS (200 OK)

Observability Metrics trace

Analyze how the Bifrost budget gate and Langfuse analytical pipeline capture and evaluate execution telemetry:

TRACE_INSIGHT: COST_ANALYSIS

💰 450x API Token Cost Savings

Scenario A is blind to code boundaries, repeatedly dispatching massive 4.5 MB frames to external APIs, resulting in $210.72 in token fees before being blocked. Under Drover, the RLM runs in Git Delta Mode, utilizing bare Go queries inside a sandboxed interpreter to refactor components for only $0.46—saving 99.7% of token fees.

🧪 The Proof: A Real-World PR Experiment

To prove the effectiveness of Drover Ontology when traversing highly complicated systems, we designed a specific refactoring PR challenge targeting the public drover-ontology Go codebase:

EXPERIMENT_SCOPE

Enforce curatedBy Schema Property

The task requires an AI agent to extend the validation engine to enforce a new strict schema metadata parameter across multiple layers:

VALIDATION ENGINE: internal/ontology/validate.go
INTERPRETER HARNESS: tools/rlm-ontology/main_rlm.go
VISUALIZER COMMAND: commands/visualize.go

STATUS: COMPLEX POLYGLOT MIGRATION

THE_OUTCOME

SCENARIO A (WITHOUT DROVER)

The agent edits the validation logic in the Go core but completely misses the visual sidebar panels and pre-seeded templates. The visualizer and CLI crash on startup.

SCENARIO B (WITH DROVER)

The agent queries the Drover Knowledge Graph first, instantly mapping the Term:validation-policy relations. It refactors all 3 directories perfectly in a single turn.

RESULT: SINGLE-TURN SUCCESS ($0.46)

🐳 Local Observability Sandbox

Run Langfuse v3 and Bifrost via Docker, then build Drover from source. Langfuse 2 reached end-of-life in early 2025; there is no published ghcr.io/drover-org/drover-visualizer image—build the harness from the drover-ontology repo instead.

01 — Langfuse v3 (official compose)

# From https://github.com/langfuse/langfuse/blob/main/docker-compose.yml
curl -LO https://raw.githubusercontent.com/langfuse/langfuse/main/docker-compose.yml

# Replace every # CHANGEME secret before production use
docker compose up -d

# UI: http://localhost:3000

02 — Bifrost gateway (config.json budgets)

Bifrost budgets are defined in config.json under governance.budgets—not via a BIFROST_BUDGETS_FILE env var. See the Bifrost governance docs.

# bifrost-data/config.json (excerpt)
{
  "$schema": "https://www.getbifrost.ai/schema",
  "providers": {
    "openai": {
      "keys": [{
        "name": "openai-primary",
        "value": "env.OPENAI_API_KEY",
        "models": ["gpt-4o"],
        "weight": 1.0
      }]
    }
  },
  "governance": {
    "virtual_keys": [{
      "id": "vk-refactor-loop",
      "name": "monorepo-refactoring",
      "is_active": true,
      "provider_configs": [{
        "id": 1,
        "provider": "openai",
        "weight": 1.0,
        "allowed_models": ["gpt-4o"]
      }]
    }],
    "budgets": [{
      "id": "budget-refactor-loop",
      "virtual_key_id": "vk-refactor-loop",
      "max_limit": 200.00,
      "reset_duration": "1M"
    }]
  },
  "config_store": {
    "enabled": true,
    "type": "sqlite",
    "config": { "path": "./config.db" }
  }
}

# docker-compose.bifrost.yml
services:
  bifrost:
    image: maximhq/bifrost:latest
    container_name: bifrost-gateway
    ports:
      - "8080:8080"
    volumes:
      - ./bifrost-data:/app/data
    environment:
      OPENAI_API_KEY: ${OPENAI_API_KEY}

# Gateway: http://localhost:8080/v1

03 — Drover harness (build from source)

git clone https://github.com/drover-org/drover-ontology.git
cd drover-ontology
make build

# Governed delta loop (Scenario B)
./bin/rlm-ontology -delta .

# Interactive visualizer — run locally from commands/visualize.go
# (no published container image)

Target Repository: github.com/drover-org/drover-ontology

🚀 Experiment Observation Playbook

01_EXECUTION_STEPS

Clone Target Codebase:
git clone https://github.com/drover-org/drover-ontology.git
Launch Langfuse v3:
Download the official compose file, set secrets, and run docker compose up -d. UI at http://localhost:3000.
Start Bifrost:
Mount bifrost-data/config.json with governance.budgets, then docker compose -f docker-compose.bifrost.yml up -d.
Simulate Scenario A:
Route a standard dynamic agent walk through Bifrost at http://localhost:8080/v1, passing your virtual key via the x-bf-vk header.
Execute Scenario B:
Run the compiled Go RLM loop in Git-Delta mode: ./bin/rlm-ontology -delta .

02_WHAT_TO_OBSERVE

Bifrost Budget Gating (HTTP 429)
Watch Scenario A's infinite loop hit the hard $200 limit and get safely blocked, recorded in logs via docker logs bifrost-gateway.
Langfuse Trace Payload Differences
Open the Langfuse dashboard at http://localhost:3000. Contrast Scenario A's massive 3.5M+ input tokens with Scenario B's compact 45K token tree.
Closed-Loop Evaluation Correctness
Check the "Evals" tab inside Langfuse. Notice Scenario A failing compilation with an Eval score of 0.0 vs Scenario B scoring a clean 1.0.

SYSTEM_BOOTSTRAP_ACTION

Deploy Governed Ingestion Loops

Ready to eliminate codebase drift and enforce architectural policies at scale? Deploy the local visualizer and deep-link your design models directly into VS Code or Cursor natively.

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