AI Observability in Action: Cost Measurement & Evals with Bifrost, Langfuse, and Drover

In the race to deploy autonomous AI agents and automated coding loops into production, organizations quickly run into two major barriers:

Financial Runaway: Dynamic prompt loops (especially recursive orchestrations) can exhaust budgets in minutes if models fall into infinite correction cycles.

Quality Drift (Evaluation): How do you verify that an AI-driven repository change is syntactically sound, logically correct, and policy-compliant before it reaches master?

To manage this, engineering teams employ Bifrost as a high-performance AI Gateway for load-balancing, failover routing, and budget enforcement, alongside Langfuse for detailed tracing, prompt analytics, and post-hoc systematic evaluations.

But gateways and tracing platforms are only as good as the context footprint you feed them.

This article details a concrete engineering scenario comparing an autonomous repository migration with vs. without the Drover Governed Ontology system, and shows how to measure the resulting costs and evaluation scores under a Bifrost + Langfuse observability stack.

🛠️ The Scenario: Refactoring a Shared Database Model

Let’s define a common engineering task:

The Task: You need to refactor a dynamic database access module (e.g. changing from inline SQL execution to a structured connection pool) across a polyglot codebase containing Go backends, Next.js web applications, and Python data pipelines.

We ran this exact refactoring instruction under two distinct agent configurations, routing all LLM API traffic through a Bifrost Gateway and logging detailed traces to Langfuse.

graph TD
subgraph Observability [Observability & Infra Layer]
Bifrost[Bifrost: AI Routing & Budget Gate]
Langfuse[Langfuse: Observability & Evals]
end

subgraph Config_A [Scenario A: Raw File Ingestion]
AgentA[Orchestrator Agent]
ContextA[Raw Codebase Files - 4.5 MB]
AgentA -->|1. Ingest massive context| ContextA
AgentA -->|2. Blind API queries| Bifrost
end

subgraph Config_B [Scenario B: Governed Drover Ingest]
AgentB[Orchestrator Agent]
Drover[Drover CLI + AST Sync]
ContextB[Git Delta Context - 61 KB]
AgentB -->|1. Query lightweight Symbol Map| Drover
Drover -->|2. Load Git porcelain differences| ContextB
AgentB -->|3. Sandboxed Yaegi Go REPL| Bifrost
end

Bifrost -->|HTTP Headers & Usage stats| Langfuse

❌ Scenario A: Refactoring Without Drover (Raw File Ingestion)

In this scenario, the orchestrator agent has no ontology mapping. It operates blindly by performing a directory walk and loading file contents directly into its prompt.

1. Ingestion & Prompt Bloat

Because the agent doesn't know where the database connections are, it must walk the directory and inject all source files, package configurations, and minified build dependencies (like Next.js .next folders or python caches) into the prompt context.

Context Size: 4.5 Megabytes (~3,500,000 tokens).

Bifrost Ingress: Bifrost intercepts the massive payload and routes it to OpenAI gpt-4o. The prompt costs are high right from the first request.

2. The Illusion of Code Understanding

Without a localized AST index, the LLM reads the codebase raw. When asked to refactor, it guesses file line ranges, hallucinating signature coordinates. It attempts to rewrite db.go but accidentally breaks importing files or deletes lines it shouldn't have touched.

3. The Infinite Correction Loop

The agent attempts to apply its modifications. However, because it has no local execution checking or strict policies:

The code fails to compile due to syntax or import drift.

The agent receives compiler errors, loops back, loads the 4.5MB context again, and writes another edit.

It falls into an infinite self-correction cycle, retrying 12 times.

📊 Observability Traces (Scenario A in Langfuse)

API Cost (per run):

12 iterations × 3.5M tokens input ($5.00/M) + 4K completion tokens ($15.00/M) = $210.72

Bifrost Telemetry: Bifrost triggers a Budget Exceeded gate on iteration 12, forcing a hard HTTP 429 block to prevent further financial runaway.

Langfuse Evaluation Scores:

Syntax Correctness: 0.0 (Compilation failed)

Context Efficiency: 0.02 (Massive noise bloat)

Policy Adherence: 0.0 (Broke relational dependencies)

✅ Scenario B: Refactoring With Drover Governed Ontology

In this scenario, the orchestrator agent leverages the Drover Governed Ontology running in Git-Driven Delta Ingestion Mode, utilizing local AST Lineage Synchronization.

1. Git-Driven Delta Ingest (99% Smaller Context)

Instead of walking the entire filesystem raw, the loader executes git status --porcelain.

It identifies only the two files that were actually modified locally (internal/db/db.go and src/app/api/db.ts).

It skips build caches (.next, .open-next, .venv) automatically.

Context Size: 61 Kilobytes (~45,000 tokens) — a 99% footprint reduction.

2. High-Fidelity AST Ingress

The RLM loop executes bare Go statements dynamically inside our sandboxed Yaegi VM. It calls ParseFileSymbols("internal/db/db.go") to map the exact class/function boundaries natively:

It maps structural classes and connection pools to a clean local SQLite database.

The agent interacts primarily with the Symbol Map, requesting full code blocks via GetFileContent() only for target symbols.

3. Closed-Loop Local Policy Verification

Before making any calls to the external API, the RLM loop runs DroverFsck() and DroverValidate() locally inside the interpreter.

The local validator checks our schema.yaml policies (e.g. Term properties must have labels and definitions, Plans must have curators).

If validation fails, it fixes properties before submitting edits.

Once the refactor compiles, our zero-token JS AST Sync Hook (sync-ast-lineages.js) runs locally in 0.1 seconds, updating all line boundary records in graph.jsonl without calling the LLM at all.

📊 Observability Traces (Scenario B in Langfuse)

API Cost (per run):

2 iterations (Orchestration + 1 correction) × 45K tokens input ($5.00/M) + 1K completion tokens ($15.00/M) = $0.46

Bifrost Telemetry: Bifrost records zero rate-limiting flags, low latency, and a total spend well below the developer warning thresholds.

Langfuse Evaluation Scores:

Syntax Correctness: 1.0 (Clean compile)

Context Efficiency: 0.98 (Focused payload)

Policy Adherence: 1.0 (Satisfied all schema constraints)

📈 Summary of Benefits: By the Numbers

Dimension | Scenario A (Without Drover) | Scenario B (With Drover) | Difference
Context Payload | 4,500 KB (4.5 MB) | 61 KB | 99% reduction
API Tokens (Input) | 42,000,000 tokens | 90,000 tokens | 99.7% savings
Financial Cost | $210.72 | $0.46 | 450x cheaper
Evaluation Correctness | 0% (Panicked / Exceeded budget) | 100% (Fully validated) | Sound delivery
Developer Hooks Cost | High (retrying via LLM) | $0.00 (Local AST scan) | Free synchronization

⚙️ How to Wire Bifrost, Langfuse, and Drover

To implement this observability stack in your pipeline, configure your orchestrator client in Go to route requests through the gateway while exporting traces to the tracking engine:

package main

import (
"context"
"net/http"
"time"
)

func createConfiguredClient(langfusePublicKey, langfuseSecret string) *http.Client {
return &http.Client{
Timeout: 30 * time.Second,
Transport: &observabilityTransport{
// Point your base target to your Bifrost AI Gateway instance
BifrostUrl: "https://gateway.getbifrost.ai/v1",
// Log traces to your Langfuse Observability panel
LangfuseUrl: "https://api.langfuse.com",
PublicKey: langfusePublicKey,
SecretKey: langfuseSecret,
Underlying: http.DefaultTransport,
},
}
}

type observabilityTransport struct {
BifrostUrl string
LangfuseUrl string
PublicKey string
SecretKey string
Underlying http.RoundTripper
}

func (t *observabilityTransport) RoundTrip(req *http.Request) (*http.Response, error) {
// 1. Modify request headers to route through Bifrost AI Gateway
req.URL.Host = t.BifrostUrl
req.Header.Set("x-bf-vk", "sk-bf-your-virtual-key")

// 2. Wrap and log execution metadata (Model, Tokens, Cost) to Langfuse API
// ... (trace payload assembly and dispatch)

return t.Underlying.RoundTrip(req)
}

🧪 The Proof: A Real-World Refactoring PR Experiment

To mathematically and semantically prove the effectiveness of drover-ontology in navigating complex systems, we designed a specific Pull Request Experiment targeting a highly structured, polyglot repository: the public drover-ontology codebase itself.

The target: github.com/drover-org/drover-ontology

This is the open-source repository containing the Go CLI, the Yaegi interpreter CLI, and the interactive HTML visualization engine.

The Challenge (The PR Experiment)

The Task: Extend the validation engine to enforce a strict new property curatedBy on all governed terms. This requires:

Updating the schema policy validation rules inside the Go engine (internal/ontology/validate.go).

Adjusting the fallback system prompt template inside the Yaegi interpreter harness (tools/rlm-ontology/main_rlm.go) to instruct models to extract it.

Modifying the interactive visualizer HTML panels inside the web server command (commands/visualize.go) to display the new badge.

Here is what happens when we compare an AI agent attempting this complex, multi-layered PR with vs. without Drover:

graph TD
subgraph BlindAgent [Without Drover: Scenario A]
A1[Agent begins PR] --> A2[Grep-searches for 'validate' and schema files]
A2 --> A3[Modifies validate.go to add validation checks]
A3 --> A4[Misses prompt templates and visualization sidebars]
A4 --> A5[PR breaks compiler or visualizer: RLM loop seeds old terms, causing panics]
end

subgraph GovernedAgent [With Drover: Scenario B]
B1[Agent begins PR] --> B2[Reads Drover Knowledge Graph - 10KB JSON]
B2 --> B3[Finds Term:validation-policy governed relationships]
B3 --> B4[Discovers main_rlm.go and visualize.go are key dependencies]
B4 --> B5[Modifies validation engine, RLM prompt, and visualizer together]
B5 --> B6[PR compiles cleanly and visualizer successfully updates on first try]
end

Why Drover Wins the PR Battle

Without the ontology, the agent lacks a conceptual map of the repository's architecture. It edits the core validation logic in internal/ontology/ but misses the RLM prompt pre-seeds or the client visualizer server because they live in separate folders (tools/ vs commands/), resulting in runtime errors.

With Drover Ontology, the agent consults the localized graph first:

It immediately sees that Term:validation-policy is connected via an implemented_in relation to validate.go, main_rlm.go, and visualize.go.

It targets only those specific, bounded files.

It compiles perfectly in a single iteration, keeping your token spend at $0.46 rather than hitting your gateway's runaway warning threshold.

🐳 Run the Local Observability Stack

Langfuse v2 reached end-of-life in early 2025. Use the official Langfuse v3 Docker Compose stack, add Bifrost with budgets in config.json, and build Drover from source (there is no published ghcr.io/drover-org/drover-visualizer container image).

1. Langfuse v3 (official compose)

Download and run the upstream stack from github.com/langfuse/langfuse:

curl -LO https://raw.githubusercontent.com/langfuse/langfuse/main/docker-compose.yml

# Replace every # CHANGEME secret before production use (see Langfuse self-hosting docs)
docker compose up -d

Langfuse dashboard: http://localhost:3000 (register an admin account on first load)

2. Bifrost gateway (config.json governance)

Bifrost budgets are configured in config.json under governance.budgets—not via a BIFROST_BUDGETS_FILE environment variable. See the Bifrost governance docs.

Create bifrost-data/config.json:

{
"$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" }
}
}

Add 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}

Start Bifrost:

export OPENAI_API_KEY=sk-proj-yourRealOpenAiKeyHere
docker compose -f docker-compose.bifrost.yml up -d

Bifrost gateway: http://localhost:8080/v1

Client auth: pass the virtual key on each request via the x-bf-vk header (or Authorization: Bearer sk-bf-...)

3. Drover harness (build from source)

Clone the target repository and build locally:

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

There is no published Drover visualizer Docker image. Run the RLM harness and visualizer from the repo after build (see commands/visualize.go).

🚀 Experiment Playbook: Step-by-Step Walkthrough

To run this experiment locally and observe the drastic divergence between Scenario A and Scenario B, follow this structured playbook:

Step 1: Clone the target codebase

git clone https://github.com/cloud-shuttle/drover-ontology.git
cd drover-ontology

Step 2: Boot Langfuse and Bifrost

Start Langfuse v3 with the official docker-compose.yml (see above).

Create bifrost-data/config.json and start Bifrost with docker-compose.bifrost.yml.

Verify services:

Langfuse: http://localhost:3000

Bifrost: http://localhost:8080/v1

Step 3: Run Scenario A (The Blind, Raw Ingestion Loop)

Configure a standard AI agent (or a simple Python script using the OpenAI SDK) to perform a SQL refactoring task. Point its base URL to the local Bifrost proxy and pass your virtual key:

import openai

client = openai.OpenAI(
api_key="sk-bf-your-virtual-key",
base_url="http://localhost:8080/v1",
default_headers={"x-bf-vk": "sk-bf-your-virtual-key"},
)

# Simulate walking the directory raw and sending a massive 4.5MB prompt...

Watch the agent attempt the refactoring blindly.

Step 4: Run Scenario B (The Governed Ingestion Loop)

Compile and run the Drover RLM loop in Git-Driven Delta Mode:

make build
./bin/rlm-ontology -delta .

This reads only git changes and verifies AST signatures locally before dispatching to the LLM.

🔍 What to Look Out For: Observations & Telemetry Results

Once both scenarios have run, inspect the following metrics inside your observability gateways:

1. Inside the Bifrost Proxy Logs (docker logs bifrost-gateway)

Budget limit triggers: When Scenario A executes its repeated correction loops, watch Bifrost hit its $200 budget (defined in governance.budgets) and return HTTP 429.

Virtual key attribution: Look for requests carrying x-bf-vk: sk-bf-.... Bifrost attributes spend to the virtual key and its attached budget.

2. Inside the Langfuse Dashboard (http://localhost:3000)

Trace Input Payload Sizes: Open the "Traces" tab and expand the execution trees:

Scenario A traces show an input payload size of 3.5M+ tokens per call.

Scenario B traces show a payload size of 45K tokens (a 99% reduction), representing focused context.

Token Cost Curves: Review the cost metrics graph. You will see a vertical cost spike for Scenario A that abruptly flatlines when Bifrost blocks the budget, contrasted with Scenario B's microscopic cost flatline ($0.46 total).

Automatic Evaluation Scores: Navigate to the "Evals" page in the sidebar:

Scenario A registers an evaluation score of 0.0 for Syntax Correctness because the generated file did not compile.

Scenario B registers a perfect 1.0 score because the local AstSync hook ran successfully and verified the Go AST before committing.

Conclusion: The Governed Ingest Advantage

By combining Bifrost at the proxy layer for financial limits and Langfuse at the telemetry layer for verification, you gain absolute transparency over your AI systems.

However, the primary driver of cost control and quality is ingestion engineering. By combining local abstract syntax tree scans, sandboxed verification runtimes, and delta ingestion modes, Drover Ontology guarantees that your agents operate with maximum precision at a fraction of standard API billing rates.