perf(workflow): intelligent query optimization and reliability improvements for workflow processing

[larryro]

Summary

This PR introduces a comprehensive overhaul of the workflow processing system with intelligent query optimization, enhanced reliability, and better debugging capabilities. The changes dramatically improve performance for workflows that filter large datasets while preventing runtime errors and improving the AI workflow assistant.

Key Improvements

Performance Optimization (10-100x faster for filtered workflows)

• Intelligent index selection based on JEXL filter expressions
• AST-based filter parsing and analysis with score-based index selection algorithm
• Optimized query builder that automatically selects the best index
• Database schema introspection tool for AI workflow assistant
• Comprehensive test coverage for query optimization system

Reliability & Error Handling

• Document size overflow prevention (validates 900KB threshold before storing)
• Enhanced LLM step debugging with variable replacement logging
• Strict JSON validation and sanitization for malformed LLM outputs
• Empty prompt validation with fallback handling
• Improved error context for workflow execution failures

AI Agent Improvements

• Database schema tool for accurate filterExpression writing
• Structured output schemas (outputSchema) for all predefined workflows
• Better tool instructions with JSON formatting examples
• Required workflow context in agent system

New Features

• Conversation auto-archive predefined workflow
• Extended variable JEXL transforms (daysAgo, hoursAgo, isBefore, isAfter)
• React performance optimizations (useMemo for automation assistant)

Technical Details

Query Optimization Architecture

• New index registry system with metadata about available indexes
• Score-based selection considering exact matches, specificity, and selectivity
• AST helpers for parsing and analyzing JEXL expressions
• Removed specialized find_* functions in favor of generic optimized finder

Validation & Safety

• Size validation preventing Convex 1MB document limit errors
• Control character detection and field name sanitization
• Prompt validation ensuring non-empty LLM inputs
• JSON structure repair for corrupted field names

Files Changed: 61 files with 3,077 additions and 527 deletions

Performance Impact

• Before: Full table scans for filtered workflows (seconds to minutes)
• After: Indexed queries with optimal index selection (milliseconds)
• Scale: Efficiently handles large datasets through proper index usage

Breaking Changes

None - all changes are backward compatible

Test plan

• Unit tests for filter parsing and index selection (334 test cases)
• Integration tests for query building system (222 test cases)
• Verified existing workflows continue to function
• Tested document size validation with large outputs
• Tested new conversation auto-archive workflow
• Verified AI assistant can use database schema tool
• Tested React performance optimizations

🤖 Generated with Claude Code

Summary by CodeRabbit

Release Notes

• New Features

  • Added database schema introspection capability for AI agents to build more intelligent workflows.
  • Introduced conversation auto-archiving workflow for automatic cleanup of stale conversations.
  • Added flexible filter expressions with smart index optimization for workflow record queries.
  • Expanded date/time transformation support for workflow expressions.

• Improvements

  • Enhanced JSON schema validation for AI-generated workflow outputs.
  • Optimized message synchronization to reduce unnecessary updates.
  • Improved error messages and context logging for AI workflow steps.
  • Better handling of large workflow outputs.

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[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request introduces a comprehensive refactoring of workflow processing and agent tooling. It adds a database schema introspection tool for Convex workflows, replaces specific query operations (find_unprocessed_open_conversation, find_product_recommendation_by_status) with a unified find_unprocessed operation supporting JEXL filter expressions, and implements intelligent index selection via AST parsing and condition extraction. The changes include new JEXL date/time transforms (daysAgo, hoursAgo, minutesAgo, parseDate, isBefore, isAfter), updates to multiple predefined workflows to use filter expressions with structured outputSchema for LLM steps, sanitization enhancements to the update_workflow_step tool, and a new get_step operation for workflow_read. Frontend components are updated with a message memoization optimization in automation-assistant and a modal replacement in gmail-create-provider-dialog. Multiple test files validate filter expression parsing and index selection behavior.

Estimated code review effort

🎯 4 (Complex) | ⏱️ ~50 minutes

Possibly related PRs

• tale-project/poc2#465 — Modifies the same workflow assistant and tool guidance code (create_workflow_agent.ts, workflow_assistant_agent.ts) to update LLM tool instructions and availability.
• tale-project/poc2#382 — Updates the same automation-assistant.tsx component message handling and context stripping logic.
• tale-project/poc2#354 — Overlaps with workflow assistant/agent stack, tool_registry, and Convex tools infrastructure changes.

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[coderabbitai]

Actionable comments posted: 30

Caution

Some comments are outside the diff and can’t be posted inline due to platform limitations.

⚠️ Outside diff range comments (3)

services/platform/app/(app)/dashboard/[id]/settings/integrations/components/gmail-create-provider-dialog.tsx (1)

263-270: Remove unused customHeader variable.

The customHeader variable is defined but never used after switching from FormModal to ViewModal. This is dead code that should be removed.

🔎 Proposed fix

-  const customHeader = (
-    <HStack gap={3}>
-      <div className="size-8 bg-background border border-border rounded-md grid place-items-center">
-        <GmailIcon className="size-5" />
-      </div>
-      <span className="font-semibold">{t('integrations.addProvider', { provider: 'Gmail' })}</span>
-    </HStack>
-  );
-
   return (

services/platform/convex/agent_tools/workflows/update_workflow_step_tool.ts (1)

320-326: Type assertion as any bypasses type safety.

The as any cast on sanitizedUpdates loses type checking. While this may be necessary due to the dynamic nature of the updates, consider defining a more specific type or using a type guard to maintain some level of safety.

services/platform/convex/lib/create_workflow_agent.ts (1)

555-555: Fix the fallback model to use a valid OpenAI model.

The fallback model gpt-5.1 does not exist. Available OpenAI models as of December 2025 are: GPT-4.1, GPT-4o, GPT-4o mini, GPT-4 Turbo, GPT-3.5-turbo, and o3-mini. When OPENAI_CODING_MODEL is not configured, the agent will fail at runtime. Use a valid model such as gpt-4.1 or gpt-4o.