Build the foundation that gives AI systems reliable inputs: data pipelines, documentation, business rules, permissions, memory, retrieval, and source-of-truth handling.
Most production agent failures are not model failures. They are missing constraints — business rules carried in four engineers' heads with no formal representation agents can query. The fix is a versioned, governed context store the data team owns instead of answers.
Eight in ten agentic AI projects stall on data, not models. Score your environment on ten dimensions before the agent surfaces the gaps. Four tiers, calibrated thresholds, structural fixes ordered before operational ones.
60% of agentic projects stall on data, not models. A 30-minute, three-tier gate — Foundation, Workflow, Autonomous — that decides what autonomy your data can actually support, with a retrofit pattern for legacy systems you cannot rewrite.
Seven patterns for moving DB2, IMS, and VSAM data into RAG: nightly EBCDIC export, CDC, federation, event sourcing, dual-write, schema-on-read, and RAG over the COBOL itself. Pick by freshness budget, not preference.
Senior engineers carry the runbooks nobody wrote. Then they retire. "Document everything" is the ask that produces nothing. A structured-interview pipeline that turns one hour into searchable institutional memory before the bus-factor goes to zero.
Most enterprise AI failures are not model failures. They are retrieval failures. Chunking, embeddings, vector stores, knowledge graphs, and the context budget — what actually breaks at scale and how to build the memory layer that holds.
RBAC was built for humans clicking pages. Agents fire hundreds of retrievals per session across permission domains the role-to-resource map never reconciled. The fix lives in the pipeline, not the prompt: pre-retrieval filters, delegated identity, RLS, audit trails that outlive ACL changes.
Business logic stored in employee heads, PDFs, and Slack threads is logic the model cannot enforce. The fix is not better prompts. It is structured rules — decision tables, rule engines, policy-as-code — that the agent calls instead of guesses at.
The primary consumer of your documentation is no longer a human. It is an agent making code changes, retrieving context, executing workflows. Treat docs as infrastructure — versioned, tested, owned — or ship guesses every time the model runs.
Most broken RAG deployments are not model failures. They are upstream failures the model is forced to ventriloquize. The fix is a data pipeline that does the judgment work before retrieval — staleness gates, canonical resolution, business rules as first-class content.