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World Congress 2026 Europe - Virtual Stage • Jul 2, 2026 • Session details

Command and Conquer: How we let an LLM control our Software

Simon A.T. Jiménez

Simon Jimenez proves treating LLMs as unreliable APIs ensures stable software. Massive prompts fail. Discover how a plan-and-fulfill architecture safely gives AI the keys to your application.

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#1 about 3 min

Letting large language models control software applications

Implementing a command pattern allows users to naturalistically manage software requirements without needing manual inputs.

#2 about 5 min

Transitioning from proprietary models to multi-provider routing

Adopting an agnostic model infrastructure prevents vendor lock-in and accommodates regional hosting constraints.

#3 about 4 min

Handling provider inconsistencies and fallback offline modes

Building robust endpoint strategies manages varying API schema compliance and offline scenarios effectively.

#4 about 4 min

Mitigating language model unpredictability and parsing issues

Extracting structural output with a custom JSON parser handles the chattiness and language drifts of large models.

#5 about 6 min

Managing token consumption and knowledge base context

Implementing scoped context limits prevents unexpected invoice spikes from massive integrations like confluence document syncs.

#6 about 3 min

Securing data with tenant endpoints and handling timeouts

Allowing custom tenant endpoints prevents information leakage while asynchronous requests require careful timeout management.

#7 about 5 min

Designing safe user experiences with human approval

Proposing operations instead of auto-executing them ensures critical data modifications are explicitly reviewed by human operators.

#8 about 5 min

Improving reliability through plan and fulfill prompting

Splitting complex tasks into a lightweight planning phase and dedicated fulfillment steps drastically improves execution reliability.

#9 about 3 min

Automating compliance reviews with specialized context prompts

Running targeted system prompts against user stories enables automated GDPR checks and generates resolution suggestions.

#10 about 5 min

Key takeaways for treating language models as APIs

Treating large language models as unreliable external APIs demands strict validation and continuous prompt versioning.

Matching moments

1:44 min

Summarizing developer experience and artificial intelligence companions

Robert Hoffmann Robert Hoffmann +1 · LIVE

1:12 min

Enhancing conversational intent through modern large language models

Nathaniel Okenwa Nathaniel Okenwa · World Congress 2025

2:21 min

Transitioning from AI co-pilots to AI-native products

Jordan Tigani Jordan Tigani · World Congress 2026 Europe

5:30 min

Building components of a real-world LLM lifecycle

Maxim Salnikov Maxim Salnikov · LIVE

3:26 min

Introducing LLMs as judges for automated testing

Sebastian Messingfeld Sebastian Messingfeld · World Congress 2026 Europe

2:08 min

Applying large language models to infrastructure tasks

Alfonso Sandoval Rosas Alfonso Sandoval Rosas · Europe 2026 Virtual

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