OverseeX

The Problem

Agent behaviour is nondeterministic, so teams were either skipping tests entirely or hand-writing brittle ones that broke on every prompt tweak. Every CI run that touched an agent also burned real OpenAI, Stripe, or SendGrid spend just to exercise the happy path.

Worse, once an agent shipped, nobody found out it had regressed until a user complained. There was no equivalent of an uptime monitor for "is this agent still doing what it's supposed to."

Constraints

• Integration had to be a single install command — no teams were going to rewrite their agent to adopt a testing tool.

• Test generation had to be grounded in real recorded interactions, not synthetic assumptions about what an agent might do.

• Mocked API responses had to be realistic enough to actually catch regressions, not just cheap enough to avoid a bill.

• Alerts needed to reach a human via webhook or Slack inside the incident window, not in a batched daily digest.

• Three portals — marketing, docs, admin — had to ship from one deployable stack, not three separate ones drifting apart.

How Nexolve Built It

OverseeX turns an agent's own production history into its test suite: an SDK records what actually happened, a generation engine turns that history into coverage, and a monitoring layer keeps watching after deployment.

1. 1

   One-line SDK instrumentation

   Installing `@overseex/sdk` (npm) or `overseex` (PyPI) is the entire integration step. The SDK intercepts LLM calls, tool invocations, and agent handoffs, streaming structured trace events to the ingest API automatically.

2. 2

   Auto-test generation from real traces

   Instead of hand-written test cases, the engine watches recorded agent interactions in MongoDB, runs a coverage analysis across every execution path an agent has actually taken (243+ in a typical deployment), and generates a Playwright + Jest suite from that history.

3. 3

   Intelligent API mocking

   Outbound calls to OpenAI, Stripe, and SendGrid are intercepted and answered with statistically realistic, context-aware responses from a pre-built corpus, so CI runs stop burning real API spend on every push.

4. 4

   24/7 health monitoring

   Agents in production are watched continuously; anomaly and regression detections fire webhook and Slack alerts the moment behaviour drifts, not after a support ticket surfaces it.

5. 5

   Usage-based billing on autopilot

   DodoPayments webhook handlers update entitlement across Free, Starter, Pro, Team, and Enterprise tiers in Postgres in real time, with full audit logging in the admin dashboard.

Tech Stack

Outcomes

• Test creation time dropped from 10 hours to 10 minutes per agent, generated from real recorded traces.

• Intelligent mocking saved teams $10k+ a month in API spend during CI, per internal platform reporting.

• 2,000+ developers instrumented an agent with the SDK, across LangChain, LangGraph, CrewAI, and AutoGen.

• 24/7 monitoring catches regressions via webhook and Slack alerts before a user files a complaint.

• Live at aiagentguard-web.pages.dev, with self-serve pricing from Free to $349/mo.