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

Unlocking the AI Black Box: Building Trust in the Era of Agentic Production

Jemiah Sius , Harry Kimpel

Are autonomous AI agents silently breaking your production code? Learn how OpenTelemetry exposes hidden LLM workflows to prevent behavioral failures, control token costs, and automate SRE operations.

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#1 about 5 min

Impact of AI on the software development lifecycle

How the pressure to increase code production significantly alters traditional software delivery cycles.

#2 about 3 min

Monitoring AI model quality and execution performance

Evaluating application interactions based on response quality, latency, and underlying model choice.

#3 about 1 min

Standardizing AI telemetry with the OpenTelemetry framework

Using an industry-standard framework to capture consistent tracing and log data from generative AI.

#4 about 3 min

Tracing agentic capabilities and step-by-step code execution

Observing what coding agents execute behind the scenes, including underlying bash scripts and token consumption.

#5 about 3 min

Debugging agentic tool calls and generation costs

Visualizing the sequence of operations, memory usage, and actual utility costs for troubleshooting prompts.

#6 about 2 min

Securing local AI development workflows against production failures

Leveraging open-source tracing locally to run safety checks on coding agents without extra overhead.

#7 about 4 min

Tracking operational challenges and incident response metrics

Connecting metric tracking directly to enterprise revenue by reducing incident investigation and mitigation times.

#8 about 3 min

Using AI for incident summaries and root cause analysis

Generating dynamic performance baselines and intelligent root cause explanations during application alerts.

#9 about 4 min

Implementing self-healing workflows for automated operations

Automating remediation workflows by deploying agents to evaluate alerts, draft fixes, and issue pull requests.

#10 about 3 min

Reducing downtime with intelligent AI observability pipelines

Integrating continuous feedback loops to detect issues instantly and resolve failures safely.

#11 about 2 min

Establishing shared team ownership for AI application observability

Distributing instrumentation and reliability responsibilities across platform teams, operations, and individual developers.

Matching moments

1:05 min

Implementing monitoring and observability for AI software deployments

Alejandro Saucedo Alejandro Saucedo · WWC 2025

1:50 min

Managing observability using natural language AI agents

Alex Laubscher Alex Laubscher +3 · WWC 2025

1:48 min

Adapting observability strategies for long-running enterprise AI agents

Christian Heilmann Christian Heilmann +3 · WWC Europe 2026

1:55 min

Identifying and fixing over-engineered AI calls through observability

diabhey diabhey · WWC 2025

1:21 min

Leveraging generative AI for application observability and security

Jemiah Sius Jemiah Sius · WWC 2023

1:09 min

Resolving developer challenges in AI agent implementation

Ricardo Ricardo · WWC 2025

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