Get Running in Minutes
No LLM needed to start. Full pipeline runs with mock provider.
Architecture Hierarchy
A deliberate, layered execution model — every component has a defined role.
Applying architecture principles, patterns, and governance to the design and execution of agentic process flows and integrations.
Framework Principles
Architecture First
Design the system before writing code. ABBs define contracts. SBBs implement them.
Governed Execution
Every agent boundary enforces governance. Policy-checked before action executes.
ABB / SBB Separation
Stable abstract contracts. Swappable concrete implementations. Eclipse-style extension points.
Zero Trust Execution
Every agent action verified and risk-evaluated prior to execution at the architecture layer.
Pluggable Providers
Ollama, Watsonx, OpenAI — swap via config. No code changes required.
Observable by Default
Every routing decision persisted. Full audit trail without extra instrumentation.
Enterprise Integration
Kafka, PostgreSQL, Neo4j, MCP. Production infrastructure, not demo infrastructure.
Vendor Neutral
No lock-in. LLM provider, messaging, persistence — all configurable, all replaceable.
CLI Experience
K9-AIF ships with a full command-line interface. Verify, inspect, generate, and run — from the terminal.
Where K9-AIF Fits
K9-AIF defines how the full agentic AI system is architected.
- Frameworks like CrewAI define how agents collaborate
- Cloud platforms like Azure and AWS provide infrastructure
- Runtimes execute workflows
About K9-AIF
K9-AIF did not begin as a grand architectural vision. It began as a retrieval capability — and evolved into an architecture-first framework for governed agentic AI systems.