FROM DEMOS TO DUTY: MAKING AI AGENTS PRODUCTION-GRADE WITH DATA, RETRIEVAL, AND GOVERNANCE
Teams are turning AI agents into dependable operators by treating them as system actors, grounding them in clean data, and stacking verification defenses. ERP ...
Teams are turning AI agents into dependable operators by treating them as system actors, grounding them in clean data, and stacking verification defenses.
ERP leaders argue AI should be a governed stakeholder, not a bolt-on, with clear roles, guardrails, and human override paths WebProNews: ERP AI as a stakeholder. Practitioners echo a systems-thinking mindset over prompt tinkering, focusing on structure, leverage points, and resistance in complex setups Business Engineer.
On reliability, a breakdown of when LLMs hallucinate most also catalogs fixes: retrieval-augmented generation, chain-of-verification, self-consistency, and fine-tuning, with stacked methods dropping errors meaningfully on benchmarks WebProNews: hallucinations and fixes. Field builds show the path: a Slack NL2SQL analytics agent over a semantic layer HackerNoon: self-service analytics agent, an agentic helpdesk using FastAPI, PostgreSQL, Ollama, and n8n orchestration DEV, and industrial data modeled for agents with Postgres/Timescale, unified namespaces, and time-series joins HackerNoon: factory data for agents.
If you want an LLM to “think with your data,” design the retrieval layer, schema, and evaluation loop first, not last HackerNoon: teach the LLM to think with your data.
Production agents need dependable data, retrieval, and governance or they will fail loudly in high-stakes workflows.
Real builds show repeatable patterns you can adopt now without waiting for the next model upgrade.
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terminal
Run an A/B on a key task with and without RAG + chain-of-verification; track hallucination rate, latency, and cost.
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terminal
Pilot an NL2SQL Slack bot against a read-replica via a semantic layer; enforce guardrails, approvals, and SLOs.
Legacy codebase integration strategies...
- 01.
Map high-value ERP entities and fix master data first; add retrieval indexes and data contracts before agent rollout.
- 02.
Wrap agents with human-in-the-loop approvals for sensitive actions; log prompts, context, and decisions for audit.
Fresh architecture paradigms...
- 01.
Standardize on PostgreSQL/Timescale for events and time-series; define a unified namespace and metadata for retrieval.
- 02.
Stand up an orchestration layer (e.g., n8n or a service bus) implementing observe-decide-act loops with metrics and fallbacks.
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