Autonomous Multi-Agent Engineering
Every line of code verified by two independent AI models. Every change passes through specialized agents. Every build validated by IDE-native static analysis.
Autonomous Multi-Agent Engineering
MCP-Orchestrated Development Workflow — click any stage to explore
Codebase Research & Exploration
Phase 1 — Map before you move
Every task begins with structured research. Up to 3 parallel sub-agents explore the codebase simultaneously — locating files, analyzing signatures, and mapping usage patterns through the IDE integration layer.
thoughts/research/YYYY-MM-DD_<topic>.md — structured findings with file:line references, architecture context, data flow, and gotchas
Impact Multiplier: Research review = 100-1000x impact. Catching wrong assumptions here prevents cascading errors downstream.
Why This Architecture
Section titled “Why This Architecture”Single-model AI development has three fundamental limitations: single point of failure (one model’s blind spots propagate), context window exhaustion (large codebases exceed session capacity), and no structural guarantees (conventions, tests, and edge cases get skipped). This architecture addresses each:
| Problem | Solution |
|---|---|
| Single model blind spots | Dual-model verification — two frontier models cross-verify every change |
| Context exhaustion | File-based context management — research writes to persistent files, implementation reads only what’s needed |
| No structural guarantees | Agent constitution pattern — specialized agents with exclusive file ownership and non-negotiable rules |
How It Works
Section titled “How It Works”Multi-Phase Pipeline
Section titled “Multi-Phase Pipeline”Every task runs through a structured pipeline with verification at each stage:
- Research → Planning → Implementation with review gates between each phase
- Impact multipliers: Research review catches wrong assumptions early (highest leverage). Plan review validates approach. Code review catches bugs.
- Context discipline: Compact after every 2-3 implementation phases, use sub-agents for exploratory searches
Dual-Model Verification
Section titled “Dual-Model Verification”Every task runs both models in parallel. Neither trusts the other.
| Task | Primary | Verifier | Mode |
|---|---|---|---|
| Architecture & root cause analysis | Model A | Model B | Parallel, compare |
| Security audit, consensus review | Model A | Adversarial challenge | Parallel, compare |
| Implementation & bug fixes | Model A | Cross-verification | Parallel, cross-verify |
| Test writing | Regression + coverage | Edge-case + adversarial | Parallel, merge |
| Code review & pre-PR gate | Standard review | Adversarial review | Parallel, merge findings |
IDE-Native Tooling
Section titled “IDE-Native Tooling”AI agents connect directly to the IDE runtime via the Model Context Protocol. Every build, inspection, and refactoring goes through the IDE — not CLI approximations.
Capabilities include: full compilation with error reporting, static inspections, semantic symbol lookup, IDE-aware rename refactoring, test execution, project-wide search, and code formatting — all through the live IDE session.
Quality Gates
Section titled “Quality Gates”Every commit must pass all gates. No exceptions, no overrides:
- Compilation clean (IDE build, not just CLI)
- Static analysis clean for all modified files
- All tests passing
- No critical or high-priority items introduced
- No forbidden patterns (thread-safety violations, raw types, catch-all exceptions)
- Null-safety annotations on all new public API
- Cross-model review passed
Infrastructure & Static Analysis
Section titled “Infrastructure & Static Analysis”Every layer of the build pipeline catches different categories of defects:
| Layer | What It Catches |
|---|---|
| Compile-time analysis | Type errors, API misuse, concurrency bugs, null-safety violations |
| Bytecode analysis | Concurrency bugs, resource leaks, security anti-patterns |
| Semantic code scanning | Injection flaws and taint-flow issues, on every push to CI |
| Dependency scanning | CVE detection in the release pipeline |
| Code coverage | Line, branch, instruction metrics |
| Container builds | Multi-stage builds, health checks, metrics exposure |
The Numbers
Section titled “The Numbers”Real metrics from production codebase — not estimates, not projections:
| Metric | Value |
|---|---|
| Production code | 239,000+ lines of Java |
| Source files | 705 across 6 Maven modules |
| Test coverage | 1,213 test files, 37,000+ individual tests |
| Protocol messages | 227 binary message types |
| Data structures | 11 distributed + 3 CP primitives + 4 CRDTs |
| Static analyzers | 6 independent analysis layers |
Tech stack: Java 25 (preview features), Spring Boot 4.1.0, Raft Consensus, mTLS/RBAC, Virtual Threads, custom binary protocol, Docker multi-stage builds, Prometheus metrics.
Browse the LoomCache documentation to see the product built by this process, or the Agentic Development page for the service overview.
LoomCache is an independent open-source project. It is not affiliated with, endorsed by, or sponsored by Hazelcast, Inc. or by any other company whose products are named in this documentation. “Hazelcast” is a trademark of Hazelcast, Inc.; references to it are nominative and describe only migration and comparison. All other product and company names are trademarks of their respective owners and are used for identification purposes only.