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

Taming the Beast: Building Autonomous Agents to Solve German Tax Bureaucracy

Mohamed Dhiab

The future of AI agents isn't bigger models—it's better engineered loops. Discover how Mika built a strict graph architecture to safely automate complex German tax bureaucracy.

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#1 about 3 min

Automating the complex German tax bureaucracy system

Navigating legacy structures, contradictory rules, and unstructured data requires high legal correctness.

#2 about 2 min

Measuring automation success with touchless booking rates

Achieving a 97 percent automation rate requires a rigorous definition of zero human edits from raw documents to final entries.

#3 about 2 min

Structuring code with deterministic graphs and smart nodes

Replacing hallucination-prone single guard prompts with strict deterministic graphs ensures legal step order while leveraging AI only for reasoning.

#4 about 2 min

Processing documents via deterministic layers and reasoning agents

Raw documents pass through duplication filters before specialized AI agents extract line items and assign standard accounting codes.

#5 about 1 min

Validating tax compliance using composable decision trees

Deterministic rules form decision trees that verify booking candidates against specific tax law paragraphs for audit readiness.

#6 about 2 min

Converting human decisions into permanent structured memory rules

A gatekeeper agent surfaces necessary to-dos, turning every human resolution into a persistent rule without requiring model fine-tuning.

#7 about 2 min

Scaling accountant capacity through AI workflow automation

Persistent memory rules enable accountants to manage eight times more customers while maintaining a high touchless booking rate.

#8 about 2 min

Modeling document-to-transaction matching as an NP-hard problem

Matching multiple invoices to multiple payments creates a computationally explosive subset matching challenge that requires simplification assumptions.

#9 about 3 min

Solving transaction matching with a layered pruning system

A layered pipeline uses deterministic filters and gradient-boosted relevance rerankers to prune pairs before an agent handles complex edge cases.

#10 about 3 min

Essential patterns for building reliable agents in regulated domains

Designing predictable loops, prioritizing cheap pruning, and replacing single prompts with specialized agents ensures compliance in high-stakes environments.

Matching moments

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · WWC 2025

3:20 min

Automating regulatory compliance through distributed AI agents

Olga Woschitz Olga Woschitz +1 · Europe 2026 Virtual

3:17 min

Building reliable AI agents for business value

Kesha Mykhailov Kesha Mykhailov · WWC Europe 2026

1:05 min

Key takeaways for architecting reliable software agents

Nimrod Kor Nimrod Kor · WWC 2025

1:19 min

Turning repetitive developer tasks into automated AI agent skills

Markus Eisele Markus Eisele · WWC Europe 2026

2:34 min

Balancing developer autonomy with the adoption of coding agents

Clemens Wasner Clemens Wasner +4 · WWC Europe 2026

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