Session

Agents in the Brownfield: Wiring an LLM Into a Twenty-Year-Old Estate

Every agent demo talks to a clean REST API with an OpenAPI spec. Then you go to work, where the system of record is a SOAP service documented in a Word file from 2011, the business logic lives in stored procedures, and the one machine nobody dares redeploy is also the one that owns your customer data.

This talk is about that estate, because that is where the value actually is.

We will work through how to give an agent useful, bounded access to legacy systems. Wrapping hostile interfaces in tools a model can reason about. Deciding what an agent may write versus only read. Where to put the seam so that a model failure cannot corrupt a system you cannot roll back. We will look at the anti-corruption layer as a tool boundary rather than a new microservice, at idempotency and compensating actions for calls that cannot safely be retried, and at how to present a forty-field record descended from a mainframe schema so that a model does not hallucinate its way through it.

Then the part that decides whether any of it ships: the team that owns the legacy system did not ask for your agent, and has good reasons to be nervous. How to get access, what to promise, what to never promise, and what a proper audit trail buys you in that argument.

Twenty years of corporate development, eight of them dragging systems onto Kubernetes. The examples are real and the scars are mine.

Takeaways

- A tool-boundary pattern for exposing legacy systems to agents without rewriting them
- Read and write authority models, and how to keep destructive operations behind a human
- Handling non-idempotent and non-retryable operations inside an agent loop
- Data shaping: making gnarly enterprise records legible to a model
- How to negotiate access with the teams who own the systems you need


Preferred duration: 45 minutes including Q&A. Can be delivered in 30 or 60 minutes on request.

Target audience: architects, senior and staff engineers, and integration engineers working in established enterprises rather than greenfield startups. No machine learning background required.

Level: intermediate to advanced.

Language: the patterns are language-agnostic; code examples are in Go.

Technical requirements: my own laptop (USB-C / HDMI) and internet access for the live demo. A recorded fallback is always available.

First public delivery: not yet delivered.

Marc Arndt

VP Engineering and Architecture at Evana AG

Heidelberg, Germany

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