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World Congress 2026 Europe • Jul 10, 2026 • Session details

Code Is Cheap. Software Isn’t.

Markus Eisele

Generating syntax with AI is cheap. Building resilient enterprise software is not. Learn how to tame coding agents with strict guardrails and dynamic context.

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#1 about 4 min

The reality of using AI coding agents in legacy codebases

Coding agents struggle to infer hidden constraints and invisible knowledge scattered throughout large enterprise applications.

#2 about 4 min

How missing context leads to subtle AI agent hallucinations

AI models invent plausible but incorrect solutions when they lack full visibility into complex enterprise architectures.

#3 about 3 min

Managing architecture ambiguity using explicit coding agent instructions

Providing explicit architectural workflows and clear documentation prevents agents from making hazardous structural assumptions.

#4 about 3 min

Delegating tasks by separating intent from final agent implementation

Separating the definition of intent from the implementation details enables static verification of generated results.

#5 about 2 min

Defining strict task boundaries and using tests as safety guardrails

Starting with tests and strict dependency limits creates firm boundaries that restrict overly creative behavior.

#6 about 3 min

Decomposing large agent requests into small agile software iterations

Breaking complex transformations into small single-step iterations keeps AI code outputs transparent and manageable.

#7 about 4 min

Using architecture decision records to formalize agent operating rules

Documenting the reasoning behind technical choices guides coding agents effectively throughout entirely long-lived software projects.

#8 about 2 min

Turning repetitive developer tasks into automated AI agent skills

Structuring repetitive review and refactoring processes into predictable agent skills avoids the pitfalls of endless conversations.

#9 about 3 min

Enhancing agent accuracy with model context protocol servers

Connecting agents to documentation indexes and live runtime endpoints significantly improves the relevance of their suggestions.

#10 about 2 min

Structuring AI-driven product development into explicit phased implementation workflows

Executing AI modifications in discrete phases of exploration, decision, and review maximizes control over systematic implementation.

#11 about 4 min

Treating generated code as untrusted contributions to prevent developer burnout

Strictly reviewing AI-generated pull requests maintains system integrity while concurrently managing the cognitive fatigue of constant supervision.

Matching moments

2:34 min

Balancing developer autonomy with the adoption of coding agents

Clemens Wasner Clemens Wasner +4 · WWC Europe 2026

5:16 min

Motivations for adopting AI to enhance developer productivity

3:42 min

The promise and risk of AI coding agents

May Walter May Walter · WWC Europe 2026

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann +2 · LIVE

3:45 min

Balancing AI tool mandates with developer trust and productivity

Chris Heilmann +2 · LIVE

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · WWC 2025

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