Why and How You Should Build Enterprise Agents In-House
Tuesday, August 4, 2026 at 4:15 PM to 4:35 PM · 20 min. (US/Pacific)
Delfino Ballroom 4104
Solo Talk
Infrastructure: AI Agents
Information
How should you deploy agents that can deeply understand your company’s fragmented data sources? The answer isn’t renting another vertical SaaS bot. It should be an agent architecture you own and control.
This session shows how to build enterprise agents as simple yet powerful modular systems. The architecture is broken into five switchable parts: the user interface where work begins, the agent harness that defines prompts and policies, the model that can be swapped, the data runtime that plans and executes retrieval, and the data sources that hold operational truth.
Internally built agents can achieve the highest accuracy and cost efficiency, while also providing strategic control. The best performing agent is the one with the best access to all of your relevant data, and wide-ranging access is best suited to agents you can fully inspect and control.
Using a devops agent that investigates alerts as the worked example, we will walk through a practical implementation using proven open source software and easily switchable models. An alert arrives in Slack; the agent states a plan; it queries observability data, error tracking, repo analysis, code changes, prior incident context; then it returns a concise diagnosis with evidence and mitigation options. Similar implementations can support security investigations, customer escalations, churn analysis, billing operations, sales operations, business intelligence, and other use cases where answers require data from many tools and systems.
The tools available now, from agent harnesses to agent data retrieval, transform the trade-offs in build-versus-buy enterprise AI decisions. You can own the asset that compounds in value from your usage; the derived intelligence mapping your systems, schemas, permissions, past queries, traces, and domain-specific patterns. If that learning sits outside the enterprise, then switching vendors can mean starting over cold. If it stays inside, each agent run makes the next one stronger.
This session shows how to build enterprise agents as simple yet powerful modular systems. The architecture is broken into five switchable parts: the user interface where work begins, the agent harness that defines prompts and policies, the model that can be swapped, the data runtime that plans and executes retrieval, and the data sources that hold operational truth.
Internally built agents can achieve the highest accuracy and cost efficiency, while also providing strategic control. The best performing agent is the one with the best access to all of your relevant data, and wide-ranging access is best suited to agents you can fully inspect and control.
Using a devops agent that investigates alerts as the worked example, we will walk through a practical implementation using proven open source software and easily switchable models. An alert arrives in Slack; the agent states a plan; it queries observability data, error tracking, repo analysis, code changes, prior incident context; then it returns a concise diagnosis with evidence and mitigation options. Similar implementations can support security investigations, customer escalations, churn analysis, billing operations, sales operations, business intelligence, and other use cases where answers require data from many tools and systems.
The tools available now, from agent harnesses to agent data retrieval, transform the trade-offs in build-versus-buy enterprise AI decisions. You can own the asset that compounds in value from your usage; the derived intelligence mapping your systems, schemas, permissions, past queries, traces, and domain-specific patterns. If that learning sits outside the enterprise, then switching vendors can mean starting over cold. If it stays inside, each agent run makes the next one stronger.
AI Transformation Tracks
Infrastructure: AI Agents
Speakers
Matt Henderson
CEOCoral