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

From minutes to seconds: Lessons learned optimizing multi-turn agentic workflows

Douglas Reiser

AI workflow latency doesn't have to bottleneck your scaling. Discover architectural tweaks, from token masking to deterministic fallbacks, that slash processing time from minutes to seconds.

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#1 about 6 min

Introducing the multi-turn CRM agent scenario

An overview of a CRM AI workflow designed to extract and link entities from conversational text.

#2 about 4 min

Analyzing the data staging orchestration pipeline

How the orchestrator separates intent between sub-agents and deterministic workflow boundaries.

#3 about 2 min

Measuring latency and token usage via tracing

Tracking step-by-step LLM inputs and costs to establish baseline performance metrics.

#4 about 3 min

Calculating latency through time to first token

How model initialization times and token generation speeds dictate overall system latency.

#5 about 9 min

Reducing output tokens by referencing text

Using indexed markers to prevent large language models from unnecessarily repeating inputs.

#6 about 2 min

Trimming context schemas to control reasoning

How converting dense JSON inputs to plain text alters hidden model reasoning layers.

#7 about 3 min

Masking redundant data tokens with placeholders

Substituting lengthy IDs and URLs with reference keys to minimize context bloat.

#8 about 3 min

Maintaining prompt structures for prefix caching

Ordering dynamic variables last to maximize cache hit rates across multiple requests.

#9 about 4 min

Decoupling workflow states from conversational history

Managing function parameters directly from execution states rather than orchestrator transcripts.

#10 about 4 min

Replacing marginal AI calls with deterministic logic

Limiting language models to unpredictable edge cases while handling standardized steps in code.

#11 about 4 min

Toggling explainability features to lower inference footprint

Stripping debug reasoning tokens from production prompts to directly decrease generation delays.

#12 about 2 min

Benchmarking quality degradation on smaller models

Scaling inference engines down and accepting minor feature lapses for substantial budget relief.

#13 about 3 min

Reviewing cumulative token and latency optimizations

Comparing the aggregate baseline times against the optimized branch configurations.

Matching moments

4:26 min

Optimizing AI infrastructure costs by maximizing token caching

Liran Hason Liran Hason · WWC Europe 2026

1:08 min

Rebuilding complex agent architectures to optimize token utilization

April Yoho April Yoho · WWC Europe 2026

2:01 min

Transitioning toward AI-first coding and managing token costs

Clemens Wasner Clemens Wasner +4 · WWC Europe 2026

3:21 min

Optimizing AI token consumption and running local language models

Chris Heilmann +2 · LIVE

2:27 min

Running agents efficiently at enterprise scale

David Soria Parra David Soria Parra +3 · WWC Europe 2026

3:06 min

Addressing the gap between coding assistants and complex workflows

Julia Kordick Julia Kordick · WWC Europe 2026

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