AI Telemetry & Auditing

Auditable Backend for
Autonomous AI Agents

Stop debugging agent swarms with messy log files. Track system prompt changes, LLM drift, and task success rates by unifying static configurations with continuous time-series metrics.

Clone Agent Auditing Blueprint Read the Docs

Why Traditional Logs Fail for AI Agents

As you deploy swarms of autonomous agents (customer support, data scraping, code writing), you create a massive observability blind spot. When an agent fails, you need to know exactly why.

✗ Impossible to correlate a hallucination spike with a specific prompt change.
✗ Logs don't tell you *who* authorized the tweak to the agent's system prompt.
✗ Time-series metrics (latency, tokens) are isolated from the static state (model version, tools).
✗ Scaling new agent types requires building custom database migrations every time.

The Agent Auditing Blueprint

Omnismith generates programmable runtime schemas. You define the shape of your Agent ecosystem on the fly, and we automatically provide the REST API, audit trails, and metric ingestion endpoints.

✓ Dynamic Templates: Define 'QA Agent', 'Scraper Agent' without migrations.
✓ Relational Schemas: Connect Agents to standalone 'Tool' records for complete flexibility.
✓ Continuous Metrics: Ingest token usage, latency, and success rates.
✓ Deploy in seconds via the Blueprint Marketplace.

Template: AI Agent

Agent Instance ID String (Unique)

LLM Version String — GPT-4o

System Prompt Markdown

Tokens Consumed Metric (Time-Series)

Template: Agent Tool Assignment

Agent Instance Reference → AI Agent

Authorized Tool Reference → Tool Catalog

Assigned By Reference → Team Member

Immutable "Drift" Auditing

LLM performance drifts when parameters change secretly. Omnismith guarantees an enterprise-grade audit trail. Every time a developer tweaks a prompt or updates an agent's model, the change is permanently logged.

✓ Track exactly *who* changed a system prompt.
✓ Diff the 'before' and 'after' state of agent configurations.
✓ Overlay audit events onto time-series metrics to correlate failures.
✓ Enforce RBAC to lock down production agent schemas.

Prompt Drift Audit Log

dev@ai-startup.com 10 mins ago

Changed System Prompt: ...you are a helpful assistant... → ...you are a strict financial auditor...

lead@ai-startup.com 2 hours ago

Changed LLM Version: gpt-3.5-turbo → gpt-4o

ci-cd-bot 1 day ago

Changed Status: Staging → Production

API-First Telemetry Ingestion

Your agents shouldn't write directly to databases. Push telemetry, token counts, and state updates directly to the Omnismith REST API from your Python backend, LangChain wrappers, or custom runtimes.

✓ High-throughput metric ingestion via HTTP.
✓ Update static entity attributes alongside time-series points.
✓ Secure personal access tokens for distinct agent instances.

# Python: Agent logging telemetry & state

import requests

base_url = "https://api.omnismith.io/v1/entities/AGENT_UUID"
headers = {
    "Authorization": "Bearer omni_agent_token_xxx",
    "Content-Type": "application/json"
}

# 1. High-speed metric ingestion (Billed as ingestion)
requests.post(
    f"{base_url}/metrics", 
    headers=headers, 
    json={"attribute_values": [{"attribute_id": "tokens_used", "value": 1450}]}
)

# 2. Standard dimension state update (Billed as dimension update)
requests.put(
    base_url, 
    headers=headers, 
    json={"attribute_values": [{"attribute_id": "last_tool_used", "value": "calculator"}]}
)