Most generative AI tutorials stop at the API call. This post is about what happens after — how you actually integrate agent functionality into the applications where knowledge work gets done.
The core framework I use organizes integration around three application types:
Conversational User Interfaces Web chat, Slackbots, SMS. These are the most familiar but have real tradeoffs around brand control, customer data exposure, and external system dependencies.
Workflow Automation Batch jobs scanning warehouse tables, structured extraction pipelines, and human-in-the-loop review workflows. Architecture here differs significantly depending on whether a human is reviewing output before it's committed downstream.
Decision Intelligence Automated analysis of KPIs against business rules, next-action recommendations, and root cause analysis. This is where LLM reasoning over private enterprise data has the highest leverage — and requires the tightest security architecture.
There's also a section on the "Games of Materialized Views" framing — the idea that enterprise software is fundamentally a hierarchy of precomputed views, and LLMs extend this by enabling dynamic views built from reasoning over data. The "real-time data" discussion is worth reading if you've ever had a stakeholder demand real-time everything without thinking through what that actually costs.
Read the full post here: - https://pattersonconsultingtn.com/blog/architecture_patterns_for_integrating_agents_into_knowledge_work.html
This is part of a series — next piece digs into MLflow as an agent server for these architectures.
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