See performance impact before you merge. Track regressions to specific commits. Ship with confidence backed by data. Agent CI brings the discipline of modern software development to AI agents.
The platform that recognizes agents are software, not models
Beyond observability. Real CI/CD that runs on every commit, PR, and merge. Automated evaluation gates. Branch protection rules. Performance regression blocking. Live agent execution environments per branch for direct interaction testing. The same workflows that ship millions of applications worldwide, now available for agents.
Complete separation of concerns. Testing stays separate from production code. Software Engineering 101. Clean business logic. Pure agent implementation. Evaluation that respects your codebase architecture.
Git becomes the source of truth. Every prompt version, every tool change, every performance metric tracked through commits. One versioning system. One repository. Complete development story.
Multi-developer coordination from day one. See whose code improved response time. Track which PR enhanced accuracy. Enable junior developers to contribute with confidence. Production-grade collaboration for production agents.
From POC to production scale. Manual testing works for prototypes; production needs systematic quality assurance. Development, staging, production all evaluated consistently. Every change measured, every regression attributed. Replace gut feelings with metrics.
Ship with confidence. Automated safety checks. Performance regression detection. Semantic drift alerts. Hallucination monitoring. Continuous improvement while maintaining stability.
Every pull request gets automated testing, every commit is tracked, and every deployment is backed by data.
Use the development workflow you already know with systematic agent validation.
Quantifiable metrics that matter to every role in the development process
Start with a proof of concept, iterate based on real data, and scale to production: all with the same codebase. No more "this works in my notebook but needs to be rewritten for production."
Every PR shows performance impact. Every merge has metrics. Every deployment tracks improvements. Make architectural decisions based on quantified evidence, not hunches about what "might" work better.
Demonstrate measurable improvement over time with clear metrics. Direct involvement in defining evaluation criteria based on real-world application goals and user requirements.
Clean architecture, separation of concerns, and developer productivity at the core
No @monitor or @track decorators, and certainly no with statements establishing context, cluttering your code. Agent CI extracts evaluation data using framework conventions, keeping your business logic 100% focused on agent behavior.
Every metric tied to a specific commit and developer. Know instantly that commit abc123 improved accuracy by 5% but increased latency by 200ms. No manual correlation needed.
Your main branch is production, staging is staging, feature branches are isolated dev environments. No configuration files; your Git structure defines your deployment pipeline.
All evaluation logic lives in .agent-ci/ config files, just like .github/ workflows. Define test cases, thresholds, and evaluation criteria without touching application code.
Pull requests automatically blocked when performance degrades. See exactly which test cases failed, why they failed, and what the baseline was. All as PR comments, just like traditional CI.
Every branch gets its own running agent environment that you can interact with directly. Test changes in real-time before merging. Share branch-specific agent instances with stakeholders for immediate feedback.
Remember when you learned to write unit tests? They live in a /tests directory, not scattered throughout your application. Why? Because mixing testing logic with business logic creates confusion about what code serves which purpose. Your agent evaluation should follow the same pattern—evaluation logic belongs in configuration files, not in your agent implementation.
Modern applications get observability through infrastructure: APM agents, service meshes, platform features. Not through manual instrumentation in every function. We believe agent development deserves the same elegance. Your code expresses intent; infrastructure measures performance. That's the separation that keeps systems maintainable at scale.
Git isn't just for code backup; it's your application's memory. Every commit tells a story. Every diff shows evolution. When we make Git the source of truth for prompts and agent behavior, we're not adding a feature. We're acknowledging that version control already solved this problem decades ago. Why reinvent what already works?
Get all industry-standard evaluations out of the box with minimal configuration and zero code. For advanced use cases, our plugin system supports custom evaluations through simple Python code and an interface protocol.
String matching for deterministic outputs, exact match and pattern-based validation, return value consistency testing
Latency measurement and threshold validation, response time tracking (min/max/exact thresholds), resource usage monitoring
Vector embedding similarity analysis, cosine similarity drift detection, semantic consistency over time
Prompt injection resistance testing, jailbreak attempt detection, security constraint validation
Output consistency across multiple runs, variability analysis for non-deterministic agents, regression detection
Multi-provider LLM integration for evaluation flexibility, configurable judge prompts and criteria, hallucination assessment
Pre-configured templates for sensitive evaluations like jailbreak detection and content filtering. Keep security logic out of your repository while maintaining full evaluation coverage.
Bring your own evaluation logic. Write custom validation functions in Python that run natively on the platform. Perfect for domain-specific requirements, proprietary metrics, or complex business logic that standard evaluations can't capture.
Objective metrics that enable confident collaboration. Multi-developer teams require quantifiable standards to coordinate changes, prevent regressions, and deploy with confidence.
Shared evaluation standards prevent merge conflicts on subjective performance. Every team member works against the same quantifiable success criteria.
Git commit-level performance tracking identifies exactly which changes caused issues and which developer introduced them for efficient debugging.
Comprehensive validation gives teams the confidence to deploy frequently, knowing that performance impacts are measured and accountable.
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Agent evaluation platforms typically inherit machine learning workflows: experiment tracking, model registries, and data science tooling. Agent CI treats agents as software applications. Version control through Git, deployment via CI/CD pipelines, debugging with standard logs and traces. The evaluation framework integrates with existing development workflows rather than requiring adoption of ML infrastructure that's not suited to collaborative agent development.
Manual agent validation works perfectly—until it doesn't. The transition from prototype to production isn't gradual; it's a phase change that breaks traditional development approaches.
This is the prototype stage, and it's perfect. One person, one agent, manual testing of happy paths. Break something, fix it, move on. Every agent project starts here, and it feels sustainable.
Complexity increases, but workarounds still function. "Did you test this against the customer support scenarios?" Manual coordination through Slack. Shared testing checklists. It's more work, but teams make it work.
Manual approaches fundamentally break. Every prompt change affects multiple conversation paths. Every tool modification impacts different agent behaviors. No human team can validate every interaction pathway.
Real users depending on consistent behavior changes everything. What seemed like a minor prompt adjustment breaks the checkout flow. A tool optimization causes the agent to hallucinate. Manual validation simply cannot cover the surface area of production agent behavior.
Sarah improved the greeting logic. John's e-commerce integration stopped working. Users noticed first. Manual testing missed the interaction.
Customer support agent handles thousands of unique scenarios. Manual testing covers maybe 50. Production exposes the gaps in real-time.
Adding new capabilities shouldn't break existing ones. But without systematic validation, every improvement risks regression.
Agent CI transforms the three-developer breaking point into a scaling opportunity. Automated evaluation gates prevent regressions. Git-based workflows coordinate team changes. Quantified metrics replace manual judgment. The same systematic approach that enabled software teams to scale to hundreds of developers, now available for agent development.
Automated evaluation covers the exponential complexity that breaks manual approaches. Every prompt change, tool modification, and agent behavior gets systematic validation.
Git commit-level tracking identifies exactly what changed and who changed it. No more guessing which update caused the regression.
Systematic validation provides confidence for developers at all skill levels. Automated gates prevent inexperienced developers from breaking production.
Schedule a demo to see how Agent CI delivers quantifiable performance improvements for enterprise development teams.