Skip to main content

BitDive: Runtime Context for AI Agents

BitDive is a runtime verification platform for Java teams and AI agents.

It captures real execution traces, gives agents runtime context through MCP, compares before and after traces after a code change, and preserves intended behavior with replay-based regression suites.

This page is the short canonical context page. For the full workflow and tool details, use the linked docs below.

The Core Workflow​

BitDive's default AI workflow is simple:

  1. inspect the current runtime behavior
  2. make one focused code change
  3. compare before and after traces
  4. update regression memory only after proof

That is the Autonomous Quality Loop.

What BitDive Gives an Agent​

  • Runtime context: real payloads, SQL, method inputs and outputs, call paths, timings, and errors
  • Concrete reproduction: exact requests derived from captured traces
  • Proof after change: before and after trace comparison against real behavior
  • Regression protection: replay-based suites built from captured traces
  • Operational discipline: rules for when to refresh a baseline and when to keep a failure red

Key Terms​

  • Runtime Context: execution evidence from real requests, including payloads, SQL, call chains, method inputs and outputs, timings, and errors
  • Deterministic Verification: proving a code change against real runtime behavior through runtime context, trace comparison, and replay-based regression checks
  • Trace-Based Testing: building replay-based regression suites from real captured traces so intended behavior stays protected in CI and local development
  • Regression Management: deciding when to create a new suite, refresh a full baseline, or replace one entry after an intended change

Operational Rules​

  • BitDive does not use AI to guess tests. It records real behavior and replays it.
  • A replay test verifies a recorded baseline. It does not capture a new trace.
  • Use "integration test" for full Spring context cases. Use "component test" only when referring to older SEO content.
  • If current docs and older blog posts disagree on wording, prefer the current docs.

Canonical Docs​

Last updated on by turmyshevd