Enterprise SaaS Platform Achieves 99.7% Agent Reliability with Composo

Composo Team

Case Study: Enterprise SaaS Platform Achieves 99.7% Agent Reliability with Composo

Industry: Enterprise Software
Company Size: 500 employees, Series C
Use Case: AI agents for customer service automation

The Challenge

A customer service platform serving 200+ enterprise clients needed to add AI agents to their product suite. Their customers - including several Fortune 500 companies - required contractual SLAs for AI accuracy and demanded transparency into agent performance metrics.

The stakes were high:

  • A single hallucinated response to an end customer could trigger contract penalties of $50K+
  • Enterprise clients required real-time visibility into AI performance metrics
  • Their existing manual QA process took 5 days per release, limiting them to monthly updates
  • Competitors were shipping agent features weekly while they struggled with quality assurance
  • They needed to demonstrate 95%+ accuracy to satisfy enterprise procurement requirements

Their head of AI had evaluated existing solutions. LLM-as-judge produced inconsistent results that couldn't support SLA commitments. Building internal evaluation infrastructure would require 6 engineers for 4 months. They needed something that worked immediately and could scale to millions of daily agent interactions.

Implementation with Composo

The team integrated Composo into their release pipeline and customer-facing dashboard in one sprint:

‍


import composo

# Initialize Composo - no custom models or training data needed
composo.init()

# Define evaluation criteria for customer service agents
criteria = [
    "Reward agents that accurately retrieve customer account information before responding",
    "Reward agents that follow the exact escalation protocol defined in the knowledge base",
    "Reward agents that provide specific resolution steps matching the identified issue type",
    "Penalize agents that fabricate policy details not present in company documentation",
    "Penalize agents that make commitments beyond defined service level agreements"
]

# Evaluate every agent interaction
score = composo.evaluate_trace(agent_trace, criteria)

# Deterministic scores enable SLA tracking and customer transparency
# Score: 0.94 (same interaction always = same score)

‍

Full implementation examples at docs.composo.ai

Release Pipeline Integration

The engineering team integrated Composo into their CI/CD pipeline. Every pull request now ran 1,000+ test scenarios through Composo evaluation before merge. Release velocity increased from monthly to twice-weekly deployments.

Key implementation details:

  • Automated test suite evaluated 50 criteria across 10 agent types
  • Any code change scoring below 0.90 triggered automatic rollback
  • Engineers received detailed explanations for any quality degradation
  • Testing that previously took 5 days now completed in 30 minutes

In the first month, Composo caught 12 issues that would have reached production, including:

  • An agent update that caused incorrect order status responses
  • A knowledge base change that broke refund policy citations
  • A model swap that degraded technical troubleshooting accuracy by 18%

Customer-Facing Quality Metrics

The team also wanted to build out a feature that enabled their enterprise customers to track quality of their AI support agents in real time. So they exposed Composo scores directly to enterprise customers through their dashboard, enabling them to:

  • View real-time quality scores for every agent interaction
  • Set custom evaluation criteria specific to their industry
  • Receive alerts when quality dropped below their thresholds
  • Generate compliance reports with deterministic scoring data

Results

First Quarter:

  • Achieved full SLA compliance across all enterprise accounts
  • Reduced QA costs by $1.2M annually (from 22 FTE to 2 FTE)
  • Increased release frequency from monthly to twice-weekly
  • Closed 3 major enterprise deals worth $4.5M ARR (quality metrics were decisive)

Production Performance:

  • 2.5 million agent interactions evaluated daily
  • Average evaluation latency: 8 seconds
  • Zero variance in scores (identical interactions always score identically)
  • 15% improvement in agent accuracy through targeted improvements based on Composo data

Customer Impact:

  • 89% of enterprise customers actively use the quality dashboard
  • Support tickets about AI accuracy decreased by 76%
  • Customer churn reduced by 23% (quality transparency built trust)
  • NPS increased from 31 to 52 among enterprise accounts

The VP of Engineering stated that Composo transformed their AI agents from a risk into a competitive advantage. They could guarantee quality to enterprise customers with real data, ship improvements rapidly with confidence, and identify exactly which agent behaviors needed improvement. The deterministic scoring was critical - it enabled both contractual SLAs and customer trust that wouldn't be possible with probabilistic evaluation methods.

Implementation details and documentation available at docs.composo.ai

start using