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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

Spin up the complete Langfuse, Bifrost, and Drover sandbox locally in under two minutes. This configuration is pre-configured to run evaluations against the public drover-ontology repository:

# docker-compose.yml
version: '3.8'

services:
  # 1. Self-Hosted Observability Analytics
  langfuse-db:
    image: postgres:16-alpine
    container_name: langfuse-postgres
    environment:
      POSTGRES_DB: langfuse
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: secretpassword
    volumes:
      - pgdata:/var/lib/postgresql/data
    ports:
      - "5432:5432"

  langfuse-server:
    image: langfuse/langfuse:2
    container_name: langfuse-server
    depends_on:
      - langfuse-db
    ports:
      - "4000:3000"
    environment:
      - DATABASE_URL=postgresql://postgres:secretpassword@langfuse-db:5432/langfuse
      - NEXTAUTH_URL=http://localhost:4000
      - NEXTAUTH_SECRET=mysecurenextauthsecretstring
      - SALT=mysecuresaltstring

  # 2. Bifrost AI Gateway & Budget Proxy
  bifrost:
    image: maximhq/bifrost:latest
    container_name: bifrost-gateway
    ports:
      - "5000:8080"
    volumes:
      - bifrost-data:/app/data
    environment:
      - PORT=8080
      - BIFROST_BUDGETS_FILE=/app/data/budgets.yaml

  # 3. Dynamic Drover Visualizer & Harness
  visualizer:
    image: ghcr.io/drover-org/drover-visualizer:latest
    container_name: drover-visualizer
    ports:
      - "8080:8080"
    volumes:
      - ./.ontology:/workspace/.ontology
    environment:
      - PROJECT_ROOT=/workspace

volumes:
  pgdata:
  bifrost-data:
Target Repository: github.com/drover-org/drover-ontology

🚀 Experiment Observation Playbook

01_EXECUTION_STEPS

  1. Clone Target Codebase:

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

  2. Launch Sandbox Stack:

    Add your OpenAI key in a local .env file and boot via docker compose up -d.

  3. Simulate Scenario A:

    Route a standard dynamic agent walk through the Bifrost proxy gateway at http://localhost:5000 raw.

  4. 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:4000. 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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