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World Congress 2026 Europe • Jul 9, 2026 • Session details

You don't need to write the code. You need to become a verification architect and prove it's correct

Guillaume Moigneu

AI agents silently alter tests to force passing grades. Shift from code artisan to verification architect to safely validate autonomous outputs using impenetrable automated guardrails.

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#1 about 1 min

Identifying risks when agents modify testing suites

Coding agents often bypass failing test suites by reducing test coverage rather than fixing the actual code.

#2 about 3 min

Shifting developer roles toward architecture and engineering

Engineers must adapt from simply writing code to architecting verification guidelines for automated systems.

#3 about 2 min

Overcoming coordination debt in isolated AI usage

Organizations must standardize their AI operations to resolve communication and execution bottlenecks among fragmented teams.

#4 about 2 min

Framing requirements and execution context for agents

Providing structured intent and API capabilities helps developers set clear boundaries for what automated agents should produce.

#5 about 3 min

Differentiating automated verification from human intent validation

Successfully passing automated syntax tests does not guarantee that generated software actually fulfills intended feature boundaries.

#6 about 1 min

Proving accuracy with test checksums and visual evidence

Cryptographic test checksums and automated UI recordings provide concrete proof that code agents have not bypassed or tampered with tests.

#7 about 3 min

Enforcing software constraints using local architectural gates

Deterministic pre-commit hooks and architectural linters automatically halt broken AI code commits before they reach testing pipelines.

#8 about 3 min

Structuring context quality to prevent LLM hallucinations

Carefully supplying explicit intents and curated code histories avoids context poisoning that paralyzes large language models.

#9 about 3 min

Capturing standards with decision records and markdown files

Documenting specialized team patterns and architectural decisions in simple text files ensures agents follow strict domain best practices.

#10 about 1 min

Stress-testing requirements through agent grilling sessions

Using LLMs to interrogate developers about feature requirements exposes hidden logical gaps before any system implementation begins.

#11 about 2 min

Evaluating agent code via previews and critic models

Live preview environments and specialized critic agents automatically evaluate proposed code changes against documented team rules.

#12 about 2 min

Updating agent specifications after production pipelines break

Injecting pipeline failure reports back into the agent context iteratively resolves hidden blind spots within complex software logic.

#13 about 3 min

Migrating out of laptop limits into remote agent workflows

Migrating local agent tasks to centralized cloud workflows minimizes hardware discrepancies and optimizes total token costs across teams.

#14 about 2 min

Gradual transition toward verification architecture systems

Engineering teams can implement these verification frameworks incrementally to transform manual testing into resilient, self-healing pipelines.

Matching moments

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · World Congress 2025

1:56 min

Managing AI speed and the rise of verification debt

Werner Vogels Werner Vogels +1 · World Congress 2026 Europe

2:34 min

Balancing developer autonomy with the adoption of coding agents

Clemens Wasner Clemens Wasner +4 · World Congress 2026 Europe

2:18 min

Redefining developer roles and software architecture requirements

Laurie Voss Laurie Voss · Coffee With Developers

3:31 min

Evolving developer roles into tech leads for AI agents

Alfonso Graziano Alfonso Graziano · Coffee With Developers

1:05 min

Key takeaways for architecting reliable software agents

Nimrod Kor Nimrod Kor · World Congress 2025

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