AI-Assisted Development
Coding agents accelerate implementation, refactoring, documentation, test creation, debugging, and code exploration.
AI Engineering
We don't just use AI to write code. We engineer workflows around it.
Wisby has redesigned the software development workflow around AI while keeping architecture, verification, and production responsibility with senior engineers.
AI-native development
The workflow changes, not the standard.
AI accelerates execution across the lifecycle. Senior engineers still define the problem, system boundaries, evidence, and decision to ship.
Agentic Workflows
Recurring systems handle scoped PR generation, issue triage, review, test/fix loops, scanning, documentation, and repository analysis.
Multi-Agent Engineering
Specialized maker, test, reviewer, and security agents work in isolated contexts before a human engineer decides what ships.
Persistent Engineering Context
Project knowledge, repository conventions, MCP connectors, domain context, and retrieval systems replace generic prompts.
Multi-agent engineering
Maker Agent → Test Agent → Reviewer Agent → Security Agent → Human Engineer
Specialized agents can work in isolated contexts and Git worktrees to perform parallel tasks safely. Autonomous does not mean unsupervised.
01Maker Agent
02Test Agent
03Reviewer Agent
04Security Agent
05Human Engineer
Persistent context
AI should understand the actual engineering environment.
Project knowledge bases, repository context, conventions, MCP connectors, domain rules, and retrieval systems ground every workflow in the real codebase.
01Project knowledge
02Repository context
03Engineering conventions
04MCP connectors
05Domain context
06Retrieval systems
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Build an engineering workflow around AI.
Bring us the roadmap, repository, or production constraint. We'll show you where AI can safely create leverage.