Arthur's ADLC: Ship Reliable Production AI Agents
Arthur Platform's Agentic Development Lifecycle (ADLC) structures agent building into planning, iterative flywheel, and governance phases with full-lifecycle evals for production reliability.
Agentic Development Lifecycle (ADLC) Framework
Arthur's ADLC provides a structured process to build agents that perform in production. It divides into three phases:
- Planning & Initial Implementation: Codify objectives, develop the initial agent implementation, and establish an evaluation baseline to measure success from the start.
- Agent Development Flywheel: Deploy to live usage, identify real-world failure modes from logs, enhance behavioral evaluation suites based on those insights, and run experiments to iterate improvements continuously.
- Governance & Operations: Implement agentic governance policies, set up proactive monitoring for drifts or issues, and use an AI control plane for oversight and interventions.
This flywheel turns observed failures into targeted enhancements, ensuring agents adapt beyond static training data.
Platform Capabilities for Reliable Deployment
Arthur equips teams with:
- Full-lifecycle evals: Continuous evaluation across all ADLC phases, not just pre-deployment.
- Open-standards based: Integrates without vendor lock-in.
- Model/Framework agnostic: Works with any LLM or agent framework (e.g., LangChain, LlamaIndex).
- Customized, Domain-Specific Evals: Tailor tests to your use case, like behavioral suites for edge cases in customer support or finance agents.
These features enable confident shipping by catching issues early and maintaining performance post-launch.
Startup Support for Production Agents
Venture-backed startups building AI agents qualify for Arthur's Startup Partner Program, offering tools to solve production reliability challenges. Backed by decades of AI expertise, it helps scale secure agents without common pitfalls like unmonitored drifts.