Lindy Performance Tuning

Lindy Performance Tuning

Overview

Improve a Lindy workflow through controlled, workspace-specific experiments. Measure the current workflow in Tasks, preserve approval and security boundaries, change one variable, run the same sanitized fixture/eval cohort, and keep the change only when predeclared latency, quality, reliability, and cost criteria pass.

Prerequisites

• Lindy workspace with active agents
• Access to agent Tasks tab (view step-by-step execution history)
• A representative, data-minimized fixture set and offline eval cohort
• Permission to inspect Tasks and restore a saved version
• An owner-approved list of invariants: confirmations, authorization, redaction, retention, and side-effect limits

Instructions

Step 1: Define the Experiment Contract

Before editing, record:

• one workflow class and representative sanitized fixtures;
• the exact variable to change and why it should help;
• baseline sample window and sample count;
• duration median/p95, outcome ratio, eval score, and workspace-reported usage/cost;
• acceptance and rollback thresholds; and
• security/approval invariants that must remain unchanged.

Use the Tasks view and Get Task Details to identify the slowest or least reliable block. Do not infer universal latency or credit values from this skill.

Step 2: Save a Rollback Point

Open Version History and identify the last known-good version. Record its timestamp or label. Restoring a version creates a new editable version; it does not erase the current history. Keep production activation and side-effecting actions unchanged until the candidate passes the evaluation lane.

Step 3: Choose One Variable

Prioritize the measured bottleneck:

Do not remove confirmation, authorization, validation, redaction, audit, or fallback steps merely to improve speed. Model availability, behavior, and pricing change; use the models currently offered in the workspace and compare them on the same cohort.

Step 4: Make the Candidate Change

Example: consolidate repeated reasoning without exposing customer data.

Before:

Step 1: Classify a sanitized support fixture
Step 2: Extract approved routing fields
Step 3: Draft an internal response recommendation

Candidate:

Step 1: Return validated JSON containing classification, approved routing fields,
        and an internal response recommendation

Use placeholders or synthetic data in fixtures:

{
  "classification": "technical",
  "productCode": "PRODUCT-A",
  "issueType": "access",
  "recommendation": "Send the approved access troubleshooting guide."
}

Exclude names, email addresses, full messages, account numbers, credentials, and unnecessary conversation history. Keep any external communication in draft or Ask for Confirmation mode while testing.

Step 5: Run the Same Evaluation Lane

1. Run offline evals against the same selected historical/reference tasks. Lindy documents these evals as simulation that does not execute real actions.
2. Use the Test Panel only with test data and sandbox/test integrations. Lindy documents Test Panel actions as real, including API calls and side effects.
3. Inspect candidate Tasks/Get Task Details for duration and failures.
4. Compare the same metrics and cohort definition to baseline.
5. Reject the candidate immediately if any security/approval invariant changed.

Step 6: Decide and Roll Out

Accept only when every predeclared criterion passes. Example decision policy:

Roll out gradually, watch Tasks against the same thresholds, and restore the saved version if a rollback criterion fires. Then change the next variable in a new experiment; never combine results from several simultaneous edits.

Optimization Patterns

Prefer Deterministic Actions for Predictable Fields

Replace AI-powered fields with Set Manually mode when values are predictable:

Measure the actual change; do not attach a fixed credit saving to it.

Bound Knowledge Base Queries

• Select the smallest result set that still passes the eval cohort.
• Use specific product/issue placeholders rather than full customer messages.
• Change result count, query text, or retrieval configuration one at a time.

Filter Triggers

Add a filter based on business-owned metadata, then test both included and excluded fixtures. Confirm that filtering reduces irrelevant tasks without dropping required work. Do not claim a percentage saving until the workspace task history proves it.

Bound Agent Steps and Loops

Use an Agent Step only when the next action genuinely requires adaptive reasoning. Keep a focused skill set, measurable exit conditions, and a fallback. For lists, set Max Cycles from the expected bounded input plus a small safety margin. Set Max Concurrent according to dependency and third-party rate limits; use 1 when order or shared state requires serialization.

Error Handling

Output

Return a performance experiment record containing:

• workflow, sanitized cohort, sample count, and measurement window;
• baseline median/p95 duration, outcome ratio, eval score, and workspace usage/cost;
• one changed variable, hypothesis, and version rollback point;
• acceptance/rollback thresholds and unchanged security/approval invariants;
• candidate measurements using the same cohort; and
• a decision of accept, reject, or insufficient evidence, with a gradual rollout plan.

Examples

An email-routing workflow shows one reasoning block as the p95 hotspot. Save the known-good version, replace only three repeated reasoning steps with one schema-constrained step, and test the same redacted fixtures. Keep Ask for Confirmation on outbound email. Accept only if the chosen p95 target is met, offline eval quality does not regress, failure tolerance passes, and workspace-reported cost does not exceed the declared threshold; otherwise restore the saved version.

Resources

• Lindy Prompt Guide
• Agent Steps
• Monitor Your Agents
• Test Panel
• Evals
• Human in Loop
• Version History
• Looping

Next Steps

Use the accepted experiment receipt as the measured input to lindy-cost-tuning; do not substitute generic model, latency, or credit assumptions.