Capabilities
Production systems for agents, RAG, enterprise platforms, generative BI, and observability. Built so operators inherit something that still works after the pilot.
AI agents & multi-agent harnesses
Design and deploy agent systems with clear roles, tool registries, approval gates, and harnesses that survive beyond a notebook demo, including multi-agent coordination.
Production RAG applications
Retrieval pipelines with chunking contracts, hybrid search, citation paths, and evaluation sets drawn from real failures, so assistants work outside the pilot.
Enterprise AI platforms
Governed platforms for teams that need access control, model routing, audit trails, and operable handoff, not a pile of prompts in a shared folder.
Generative BI & intelligence layers
Agents on top of semantic layers and metric catalogs: ask questions of data without inventing KPIs or bypassing the definitions the business already trusts.
AI observability platforms
Traces, eval drift, tool-error taxonomies, and run-level visibility so operators can see why an agent or RAG path failed, and then fix it.
Code reviewers & autonomous cloud agents
Reviewers that enforce standards in the pull-request path, and cloud agents that run with bounded permissions, durable state, and kill switches in production environments.
FAQ
- What kinds of AI systems does Knackline write about and build?
- AI agents and multi-agent harnesses, production RAG applications, enterprise AI platforms, generative BI intelligence layers, AI observability, code-review agents, and autonomous cloud agents.
- How is this different from a model-demo or prompt workshop?
- We focus on the substrate that keeps AI systems honest in production: retrieval quality, tool permissions, eval harnesses, observability, and deployment, not only the happy-path conversation.
For concrete examples of how these areas show up in practice, read the reports.