Kopernik Labs Autonomous Modernization Specification

A comprehensive technical guide to autonomous legacy code modernization. Kopernik treats code migration not as generative autocomplete, but as a deterministic graph transformation problem verified by existing unit and integration test suites.

Open Live Sandbox Dashboard

Private Beta — Design Partner Validation

This specification describes the architecture Kopernik Labs is building and validating together with its design partners during the private beta. Interactive examples across this site — including the sandbox dashboard, telemetry streams, and sample pull requests — are illustrative previews of the target system, not records of live production runs. Capabilities go live progressively as pilot repositories are onboarded.

1. System Architecture & Core Philosophy

Standard generative AI models fail when applied to legacy enterprise migrations. Generating an entire codebase from scratch creates untracked hallucinations, breaks hidden domain invariants, and dumps monolithic 40,000-line pull requests that engineering teams cannot review.

Kopernik Labs decouples the modernization pipeline into a Topological Directed Acyclic Graph (DAG):

DETERMINISTIC COMPILATION PIPELINE

1. AST Ingestion

Tree-sitter & LibCST parse syntax trees

2. Tarjan SCC

Isolates circular import cycles

3. Opus 5.5 Swarm

Prompt cache context reasoning

4. Docker Loop

Self-healing test runner execution

5. Atomic PRs

50-250 lines reviewable diffs

2. Anthropic Claude Opus 5.5 & Prompt Caching

Kopernik Labs is powered by Anthropic's flagship Claude Opus 5.5 reasoning model. Long-horizon code refactoring requires deep multi-step deduction, tracing types across hundreds of files without dropping domain invariants.

Prompt Cache Strategy

We pre-cache up to 200,000 tokens of your codebase AST, schemas, and type definitions. Subsequent agent turns read the cached prompt at 90% reduced cost ($0.20/M read tokens) and sub-second latency, enabling real-time swarm deliberation.

Sub-Agent Specialization

Rather than a single monolithic prompt, four specialized Opus 5.5 sub-agents run in parallel: Dependency Analyzer, Type Synthesizer, Test Interceptor, and Atomic PR Packager.

3. 5-Stage AST Migration Engine Deep-Dive

Stage 1: Concrete Syntax Tree (CST) & Call Graph Ingestion

We parse raw source code using Tree-sitter and LibCST. Comments, whitespace, and docstrings are preserved. Every function definition, method invocation, and import is registered into a global directed symbol table.

Stage 2: Topological Ordering & Tarjan SCC Cycle Resolution

Legacy backends are riddled with circular imports (`A imports B`, `B imports A`). Kopernik runs Tarjan's Strongly Connected Components algorithm to find all cycles, automatically synthesizes abstract interfaces or event protocols, and decouples them into independent, acyclic migration layers.

Stage 3: Claude Opus 5.5 Context-Aware Transformation

The model transforms isolated leaf modules first, injecting strict types (PEP-484, TypeScript 5.5, Jakarta EE annotations) and replacing deprecated APIs (such as `urllib2`, `javax.servlet`, or synchronous Express callbacks).

Stage 4: Ephemeral Docker Regression Sandbox

The transformed code is mounted into an ephemeral Docker container mimicking your target production runtime. The existing test suite runs. If tests fail, the stderr traceback is fed back to Opus 5.5 in a self-healing loop until 100% green.

Stage 5: Atomic PR Synthesis & Invariant Proof

Once verified, Kopernik creates an atomic GitHub branch and pull request averaging 50 to 250 lines of diff, complete with verification logs, AST diff metrics, and plain-English architectural explanations.

4. CLI Quickstart & Developer Tooling

Engineers can run Kopernik directly in their local terminal or inside CI/CD pipelines using the official CLI:

$npm install -g @kopernik/cli

# Scan repository, build AST, and detect dependency cycles

kopernik scan . --target=python3.12 --output=ast.json

Traverses the working directory, constructs call graphs with Tree-sitter, detects circular imports via Tarjan SCC, and outputs an AST dependency report.

Environment Configuration (.env)

KOPERNIK_API_KEY=kop_live_948f2190...

ANTHROPIC_API_KEY=sk-ant-api03-... # Optional custom key

KOPERNIK_DIFF_CAP=250 # Max lines per pull request

KOPERNIK_SANDBOX_TIMEOUT=300 # Sandbox timeout in seconds

5. Supported Runtimes & Framework Matrix

Kopernik Labs specializes in mission-critical backend languages and framework migrations:

Ecosystem	Legacy Baseline	Target Modernization	Verified Frameworks
Python	Python 2.7 / 3.6 / 3.8	Python 3.12+ (PEP-484)	Django 1.11→5.0, Flask→FastAPI, SQLAlchemy 1.3→2.0
Java / JVM	Java 8 / 11	Java 21 LTS (Virtual Threads)	Spring Boot 1.5/2.x→3.3+, javax.→jakarta., Hibernate 6
Node / TS	CommonJS / Node 14	Node 22 LTS / ESM Native	Express callbacks→Fastify async, TypeScript 5.5, Zod v3
Go / Backend	Legacy PHP / Go 1.14	Go 1.23 Standard Library	PHP 5.6→8.3 / Microservice extraction, pgx connection pooling

6. Tarjan SCC Cyclic Dependency Resolution

When Module A imports Module B, and Module B imports Module A, naive refactoring creates broken runtime initialization order. Kopernik executes Tarjan's Strongly Connected Components (SCC) algorithm:

1. Cycle Detection

Graph DFS identifies strongly connected subgraphs with recursive cross-references.

2. Interface Extraction

Claude Opus 5.5 extracts shared data schemas or abstract protocol types into a third module.

3. Acyclic DAG

Original modules import the abstract protocol, breaking the cycle and allowing clean staging.

7. Isolated Docker Ephemeral Test Execution

All transformations execute inside ephemeral containers backed by memory ramdisks. Nothing is pushed to your Git remote until the test suite passes with exit code 0.

# Execution Pipeline:

$ docker run --rm --network=none -v /tmp/ramdisk/repo:/workspace sandbox:py312 \

bash -c "pytest -q tests/test_payment_gateway.py"

================== 18 passed in 0.42s ==================

✔ Regression check green. Generating cryptographic invariant receipt.

8. Enterprise Security & Zero Retention SLA

Kopernik Labs operates under strict enterprise trust guarantees:

Zero Model Training

All foundation model calls use Anthropic's Zero Data Retention commercial endpoints. Your proprietary backend algorithms are never stored or used to train public LLMs.

Mutual NDA Guaranteed

We sign bilateral NDAs before any repository access is granted. Repositories run in isolated tenant sandboxes with read-only git tokens.

Priority Engineering Lead Escalation Line

Direct email channel with founder & engineering lead Emre Bekir. All customer engineering teams receive guaranteed 2-hour response SLAs on active modernization pipelines.

Email bekiremre@koperniklabs.com →