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Freshworks

Multi-Agent Report Synthesis

From weeks of analyst work to minutes — a 7-agent pipeline for executive reporting

Agent Pipeline

7-Agent

Charts Processed

50+

Time Reduction

Weeks → Minutes

The Challenge

Enterprise analytics teams were manually extracting data from 50+ charts per report, synthesising insights across metrics, and writing executive narratives — a process taking weeks per reporting cycle. The workflow was fragile, non-repeatable, and scaled poorly as Freshworks' enterprise customer base grew. There was no system for cross-metric correlation detection or multi-dimensional root-cause analysis at report level.

My Role

Led the design of the full 7-agent pipeline architecture, from metadata extraction through executive summary generation — the current engineering reference for this next-horizon product, actively in development.

  • Conducted PRD gap analysis against the existing single-chart summariser
  • Defined gold-standard output quality benchmarks
  • Designed the prompt editor UI and cost/token tracking dashboard
  • Spec'd the PDF export feature

The Solution

A 7-agent pipeline, one specialised job per agent:

  • Agent 1 — Metadata Extraction
  • Agent 2 — Data Validation
  • Agent 3 — Trend Detection
  • Agent 4 — Anomaly Flagging
  • Agent 5 — Benchmark Comparison
  • Agent 6 — Cross-Metric Correlation & Multi-Dimensional RCA
  • Agent 7 — Executive Narrative Generation

It prioritises significant metric movements across 50+ charts simultaneously and generates executive-ready narratives with root-cause attribution — reducing analyst work from weeks to minutes.

The Outcome

Target: reduce report generation from weeks to minutes for enterprise analytics teams, extending the Freddy AI Summariser's single-chart capability to full report intelligence. Positioned as the next major monetisation lever for Freshworks' analytics platform. Gold-standard benchmarks are defined; engineering build is underway.

Key Learnings

  • Agent specialisation — one job per agent — dramatically improves reliability, debuggability, and the ability to evaluate each stage independently against gold-standard outputs.
  • Gold-standard output benchmarks must be defined before the build begins, not after — they are the product spec for agentic systems, not an afterthought.
  • Orchestration and error handling are harder problems than the individual agents; budget at least as much design time for the seams as for the agents themselves.

Technologies & Methods

  • CrewAI
  • LangGraph
  • OpenAI
  • AgentOps
  • Python
  • FastAPI
  • React