About#

Use this section when you want NeMo Flow to collect runtime signals and activate adaptive behavior through the plugin system.

Adaptive optimization uses the same runtime model as the rest of NeMo Flow: instrumented scopes and calls emit events, subscribers and learners observe those events, request intercepts can add hints, and plugin configuration controls activation. The adaptive component coordinates state, telemetry, adaptive hints, tool parallelism, cache-governor behavior, and rollout policy.

Agent workflows often repeat similar work, call tools with different dependency patterns, and send prompts with stable and variable sections. Adaptive optimization gives the runtime a place to observe those patterns and expose controlled behavior changes without hard-coding optimization logic into every application.

Start Here When#

Use these signals to decide whether this documentation path matches your current task.

  • Collect runtime signals before changing behavior

  • Evaluate tool parallelism opportunities

  • Add model-request hints in a controlled way

  • Plan prompt-cache breakpoints for supported providers

  • Share adaptive state across workers when needed

  • Roll out optimization through config instead of code changes

If instrumentation is not in place yet, start with Instrument Applications or Integrate into Frameworks.

Guides#

Use these guide links to move from the overview into task-specific instructions.

Validate the basic workflow before tuning lower-level adaptive settings. First confirm that instrumented work emits lifecycle events with the configuration in Basic Guide: Configure Adaptive Optimization. After that baseline is visible, keep tool parallelism in observe_only, leave cache planning disabled until you have stable prompt samples, and enable active behavior one area at a time.

Treat every adaptive change as a measured rollout. Record a baseline, change one setting, compare events and reports, and keep rollback as a configuration change.

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