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

Completing the Feedback Loop

Nimrod Kor

LLMs can't grade their own work. Move beyond basic prompt responses by building multi-layered agent workflows. Learn how external grounding drives true autonomous task completion.

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#1 about 2 min

Understanding the necessity of AI agent feedback loops

An overview of why agents often fall short and how providing appropriate context guarantees success.

#2 about 2 min

Expanding AI agents for code review and security scanning

Specialized software agents automatically manage code reviews, scan for security vulnerabilities, and handle repository merges.

#3 about 1 min

Essential components of the reason and act agent loop

Core agent architectures depend on language models paired with custom tools and the standard reasoning loop.

#4 about 1 min

Defining tools and response formats in application code

Implementing code frameworks requires explicitly defining tools, system prompts, and structured response classes for reliable outputs.

#5 about 2 min

Designing effective custom tools with descriptions and string outputs

Detailed phrasing within a tool's description directs models appropriately while formatting complex results as strings prevents constraint errors.

#6 about 3 min

Structuring thinking and verdicts in code merging agents

Ordering logical operations carefully ensures automated merge systems consider all evidence before rendering final decisions.

#7 about 2 min

Differentiating bounded agent loops from continuous agentic loops

While standard loops process bounded single tasks, an agentic loop continuously monitors environments to trigger new automated responses.

#8 about 2 min

Breaking large requirements into manageable orchestrator tasks

Separating massive engineering requests into product requirement documents empowers automated orchestrators to securely manage isolated execution threads.

#9 about 2 min

Implementing automated spec reviews across complex application environments

Integrating browser endpoints and external workflow trackers introduces broader context for evaluating sprawling new feature requests.

#10 about 2 min

Overcoming spec fatigue and hallucinated requirements in single agents

Dumping massive specification lists directly into isolated models causes missed constraints and hallucinated logic blocks.

#11 about 3 min

Scaling validation with map reduce workflow architectures

Distributing requirement verifications into parallel checks prevents duplicate evaluations while dramatically accelerating completion times.

#12 about 1 min

Optimizing efficiency by matching specific models to workflow steps

Sourcing heavy models for context extraction while matching lightweight models to isolated validations minimizes costly token usage.

#13 about 2 min

Validating code outputs against actual user interface reflections

Correlating logical structural modifications directly with integration tools reveals the disconnects within positive self-evaluations made by language models.

#14 about 2 min

Strategizing comprehensive output validation and tool optimization

True feature validation demands custom task dissections paired with concrete external test components to guarantee proper code integrations.

#15 about 2 min

Closing deployment feedback gaps with reality based behavioral checks

Exposing code bases to authentic behavior queries protects deployment confidence against severely flawed model self-grading parameters.

Matching moments

2:06 min

Rethinking team structures around AI agent capabilities

Mike Mike · WWC 2025

1:19 min

Turning repetitive developer tasks into automated AI agent skills

Markus Eisele Markus Eisele · WWC Europe 2026

2:09 min

Creating reliable feedback loops and automated testing for AI

Clemens Wasner Clemens Wasner +4 · WWC Europe 2026

2:40 min

Reviewing live performance of self-correcting AI engineering agents

Ingo Eichhorst Ingo Eichhorst · WWC Europe 2026

10:17 min

Discussion on AI hallucinations and practical developer workflows

Akmal Chaudhri Akmal Chaudhri · LIVE

1:55 min

Shifting developer workloads and realistic AI productivity gains

Chris Heilmann +2 · LIVE

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