AI coding that scales
without losing control.
FRYTAG builds a living knowledge graph from your source code, build structures, and complete Git history — and puts every coding agent to work on top of it. Agent-agnostic by design: Copilot, Claude Code, Codex, and more plug in through one governed gateway instead of replacing your tools.
Put yourself in the driver seat of your Agentic AI Coding.
AI coding that works in production, not just demos
FRYTAG promises one thing: that AI coding can scale within enterprises without losing architectural discipline, accountability, or delivery confidence. It is not a research demo, and it is not a toy.
What you get is a production runtime where enterprise AI coding becomes repeatable, controlled, and measurable. FRYTAG builds on concrete company experience — operational publishing patterns, model routing, and governance that actually works inside a serious organization.
The FRYTAG Enterprise Knowledge Layer
Code repositories, documents, tickets, and static data flow through the FRYTAG Inference Manifold Gateway into one living Enterprise Knowledge Graph — linking source code, commits, APIs, architecture decisions, runtime behavior, and organizational context, with Knowledge Capacity and Memory that grow with every change.
Today the graph already learns continuously from source code, build structures, and the complete Git history of your repositories — every commit deepens the understanding of how architecture, code, tests, CI, and organizational responsibility connect, and how a code change flows through testing, deployment, and runtime impact. Documentation, ticket, and static-data knowledge is onboarding next, converging in the same graph.
- Source code, CI pipelines, and release history — live today
- Documentation, tickets, and static data — onboarding next
- Test history, service dependencies, and runtime profiles
- Organizational responsibility mapped into every layer
- Continuous learning with every code change
Everything connects through the Inference Manifold Gateway
One entry point — MCP, A2A, and API — brings your code repositories, documents, tickets, and static data in, and connects your tools and agents out: on-premises and off-premises alike.
Cross-cutting by design: Telemetry, Tracing, Governance, Security, and Zero Trust run through every path — model usage, tokens, latency, and costs are measured, not promised.
Analyze once, reuse everywhere. Agent-driven analysis scenarios run against the knowledge graph via A2A — once. The results live in a central place where the whole development team reuses them. That is where the gold is: save time and save tokens.
- FRYTAG Agentic Harness: Ops Skills from design to production
- Specialized teamwork — multiple agents collaborating on one graph
- Integrated tools: execution and results stay traceable
MCP · A2A · API
Knowledge Capacity · Memory
Ops Skills · Teamwork · Tools
A complete method for AI-driven delivery — built in, not bolted on
The harness carries the whole engineering effort, not just the code: what to build, how it holds together, how it changes as you learn. Decisions stay explicit and carry forward as durable context between phases and agents. The process right-sizes itself — small changes go straight to build, complex initiatives get planning depth — on one delivery path from idea to tested, reviewed implementation.
Expert perspectives on demand: analyst, product manager, architect, developer, and test architect — multiple specialized agents collaborate through structured workflows and reviews, without you handing over judgment.
FRYTAG Autocoder. Push a ready epic/spec before you go to bed. Wake up to an evidence-backed pull request — or a clear, actionable failure report. A2A-driven sub-agent tasks — implementer, reviewer, story generator — run in an isolated worktree with enforced path boundaries, exit codes authoritative instead of agent self-report, and bounded repair. Sprint mode chains stories overnight, stopping at human gates. The Autocoder never merges — human merge authority is a governance invariant.
FRYTAG Systems Augur. The Augur infers and validates a C4 architecture model from the code itself, compiles a thorough review plan, dispatches sub-agent reviews through 10 specialist lenses, and assembles the findings into a Well-Architected executive summary. Every review writes back into the FRYTAG Knowledge Graph — architecture models, findings, and decisions persist in Knowledge Capacity and Memory, where the whole team and every agent reuse them. Analyze once, reuse everywhere.
From understanding to autonomous delivery
FRYTAG works in four progressive levels — each building on the last, creating a path from raw system understanding to autonomous AI-assisted engineering. Human control is scaled deliberately at every step.
Level 1 — Visibility, Recovery & System Understanding Regain control over the system and build a machine-readable understanding of your software landscape: repository analysis, architecture mapping, documentation ↔ code comparison, impact analysis — and AI coding assistance grounded in real knowledge.
Level 2 — Behavioral Stabilization Make existing behavior machine-readable and verifiable: golden-master tests, regression baselines, and API contract tests — a safety net for every future change.
Level 3 — Spec-Driven Assisted Refactoring Modernize and transform in a controlled way against specified target architectures: cross-repository refactorings, API migrations, service splits — automated against verified behavioral tests.
Level 4 — Spec-Driven Autonomous Delivery Deliver features largely autonomously from business specifications: multi-agent planning, automatic test derivation, end-to-end implementation — humans set the guardrails and monitor.
Security, Governance & Compliance — the foundation for all levels: policies, auditability, human oversight, security, data protection, and regulatory requirements.
"FRYTAG is our answer to controlled transformation. We didn't build another chat shell — we built an Enterprise AI Harness that connects repositories, documentation, and operational know-how into one semantic context."— Martin Jay, first-day user of FRYTAG.AI
Prevent (Model) Vendor Locking
For many enterprise use cases, non-frontier models — such as mini or even nano models from major providers, or open-source models on cost-effective hardware — are sufficient today and will continue to be in the future.
FRYTAG enables the use of different models for different purposes, from frontier to local on your own hardware, chosen as the situation demands. The FRYTAG Inference Manifold Gateway brings measured transparency into model usage, tokens, latency, and costs — with governance, security, and Zero Trust built in. Don't get squeezed into a setup you cannot undo later.
What makes FRYTAG different
Three commitments that guide every feature, every decision, and every deployment.
Model Agnostic
FRYTAG works with any AI model — open or proprietary. You choose the right tool for each task without lock-in.
Governance First
Human-in-the-loop oversight at every step. Policies guide, not block. Control that scales with your organization.
Production Ready
Built from real enterprise experience. Handles legacy systems, modern platforms, and everything in between.
From demo to enterprise reality.
FRYTAG moves AI from playground to production — with the same rigor that made concrete one of the most transformative materials ever created. Get back into the AI driver's seat of your software engineering.
Proven in practice: the FRYTAG core was developed and scientifically evaluated under ZIM funding together with Hochschule Albstadt-Sigmaringen and an industry partner — and is now in continuous dogfooding on real repositories.
Learn about us — Our Story See it at work — Use Cases Make it yours — Extend