Build AI on solid ground, not shifting sand

The testing and monitoring infrastructure that transforms unreliable LLM experiments into production-grade systems. For engineers who need their AI agents to actually work.

Start Testing Locally - 5 Minutes See Architecture Overview
🤖

Same Prompt, Different Results

Identical inputs lead to wildly different outputs. Your perfectly tested agent becomes a chaos generator in production.

🔧

Hidden Function Failures

Tool calls fail silently. APIs timeout without alerts. Critical workflows break while dashboards show green.

💸

Uncontrolled Cost Spirals

That innocent prompt change just 10x'd your OpenAI bill. Token consumption explodes without warning.

AI agents are unpredictable and opaque in production

You’re using yesterday’s monitoring for today’s AI—and it’s not working.

😱

Customer-Discovered Failures

Your users become involuntary QA testers. They find the bugs. They lose trust. You lose sleep.

🔍

Investigation Chaos

6-8 hours debugging per issue. $50K+/month in wasted tokens. Issues repeat across teams.

😨

Deployment Fear

2-3 week deployment cycles. 30-40% failure rate. You pray deployments work instead of knowing they will.

The CoAgent Solution

Investigation and validation tools designed for AI systems.

🔍

Investigation Tools

Debug with precision:

  • ▶Log search & correlation across all traces
  • ▶Topic modeling & user intent analysis
  • ▶Parallel testing of configurations
  • ▶Pattern recognition at scale
✅

Validation Framework

Know what's working:

  • ▶Test assertions with success criteria
  • ▶Continuous monitoring & drift detection
  • ▶Root cause analysis with context
  • ▶Semantic output validation

Core Capabilities

Built for engineers who need AI systems that actually work in production.

1️⃣

Intelligent Test Orchestration

Run parallel configurations while tracking dependencies. Test models, prompts, and tools together—not in isolation. Surface patterns across hundreds of tests.

2️⃣

Full-Context Debugging

Search across all logs and traces. Annotate failures with team insights. See token-level decisions, tool call sequences, and context degradation.

3️⃣

Assertion-Based Validation

Define what 'working' means for your use case. Semantic assertions, output validation, cost boundaries. Know immediately when reality diverges from expectations.

4️⃣

Pattern Recognition at Scale

Automatic topic modeling reveals what users are actually trying to do. Spot emerging issues before they become incidents. Track behavior drift over time.

The business case writes itself

For a team with 5 engineers and $20K monthly AI spend: Monthly value created: $53,000 | CoAgent cost: $49-299/month

Stop hoping. Start knowing. Your competitors aren’t smarter. They just have better foundations.

180x

Typical ROI

30min

From alert to fix

<10%

Failure rate

✓ With CoAgent vs ❌ Without

✓ 30 minutes from alert to fix (vs 6-8 hours debugging)
✓ Optimal token usage (vs $50K+/month wasted)
✓ Deploy daily with confidence (vs 2-3 week cycles)
✓ Patterns identified automatically (vs issues repeat)

What Engineering Teams Say

  • Finally, production-grade AI testing

    "CoAgent caught issues our tests completely missed. Parallel testing revealed our GPT-4 config was burning 10x the tokens of Claude for worse results."
    Platform Lead, Series C AI Company

  • From chaos to control in weeks

    "We went from praying deployments work to knowing they will. CoAgent turned our experimental AI into production infrastructure."
    Senior AI Engineer, Fortune 500

  • The ROI was immediate

    "First week: found $30K in wasted tokens. Second week: prevented a production outage. CoAgent paid for itself 100x over."
    CTO, AI-First Startup

Stop hoping. Start knowing.

Your competitors aren’t smarter. They just have better foundations. Self-hosted. No vendor lock-in. No BS.
Install CoAgent Now (5 Minutes)