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Kiro is a spec-driven agentic coding tool for IDE, CLI and web that turns prompts into specs and catches bugs with property-based tests.
Google's agentic development platform and IDE for building software with autonomous, Gemini-powered coding agents.
AI governance and observability platform with 100+ automated tests and real-time guardrails to evaluate and monitor ML/LLM systems.
AI observability and evaluation platform to trace, evaluate and improve LLM agents in production, with an open-source Phoenix core.
Enterprise AI observability and security platform to monitor, evaluate, and govern agentic and ML systems with guardrails.
No public pricing
- ✦Spec-driven development (requirements, design, tasks)
- ✦Parallel agents, local or cloud
- ✦Property-based and correctness testing
- ✦Works in IDE, CLI, web and mobile
- ✦Multiple models (Claude, open-weight, Auto)
- ✦Headless CLI for CI/CD
- ✦Context from tools like Figma and Terraform
- ✦Agent-first IDE experience
- ✦Autonomous planning and code execution
- ✦Integrated editor, terminal and browser control
- ✦Powered by Google's Gemini models
- ✦High-level developer supervision
- ✦100+ automated AI tests
- ✦Offline evaluation and CI/CD for AI
- ✦Real-time observability and tracing
- ✦Guardrails against PII leaks, injection, hallucination
- ✦Data-quality and drift monitoring
- ✦Compliance/governance alignment
- ✦Git, SDK, CLI and REST API integration
- ✦Agent and LLM tracing
- ✦Large-scale evaluations
- ✦Open-source Phoenix observability
- ✦Alyx AI engineering agent
- ✦OpenTelemetry-based instrumentation
- ✦Experiments and prompt playgrounds
- ✦End-to-end agentic and ML observability
- ✦Real-time guardrails (hallucination, PII, jailbreak)
- ✦Continuous evaluations and custom judges
- ✦Root-cause analysis and decision lineage
- ✦AI governance, risk, and compliance controls
- ✦Flexible SaaS, VPC, or on-prem deployment
- →Turning prompts into maintainable, spec-matched code
- →Catching bugs unit tests miss
- →Reviewing PRs and fixing bugs in CI/CD
- →Building apps with AI agents
- →Automating multi-step coding tasks
- →Prototyping and iterating on software
- →Assisting developers on complex work
- →Evaluate models before production
- →Monitor live AI systems for issues
- →Prevent unsafe or non-compliant outputs
- →Catch data drift and quality problems
- →Debugging AI agents in production
- →Measuring LLM output quality
- →Catching regressions before deploy
- →Monitoring production AI agents
- →Enforcing safety guardrails on LLM apps
- →Evaluating and debugging model behavior
- →Governance and compliance for enterprise AI