Understand The Adaline Method for building reliable AI agents.
‘The Adaline Method’ is a continuous cycle that maps the core steps of the AI Development Lifecycle (ADLC) into Adaline.
Unlike traditional software development lifecycle (SDLC), ADLC poses unique challenges such as AI agent’s being non-deterministic, prone to hallucinations, expensive, cyclical in nature, etc.
Building fast and safe AI agents requires a systematic approach prioritizing intensive monitoring with iterative development and testing.The 5 key steps are:
Instrument → Log traces and spans from your AI agent into Adaline
Monitor → Analyze your AI agent’s quality, performance, and create usable datasets from production traffic
Iterate → Fix and refine your prompt based on the patterns and issues
Evaluate → Test and verify improvements and refinements
Deploy → Ship changes in real-time to all your AI agents
These steps form a closed loop that drives ongoing improvment by each step seeding the next with data and feedback.
Send traces and spans from your AI agent by integrating Adaline via popular AI frameworks and providers, SDKs or API.
Each trace and its spans captures your AI agent’s entire workflow and state including inputs, outputs, latency, status, LLM parameters, tool executions, retrieval operations, etc.Follow one of these guides to ensure your AI agent is instrumented correctly and sending logs to Adaline.
We recommend using the integrations with AI frameworks and providers or Adaline Proxy for the quickest setup, with a few lines of code.
If you need more control, you can use the Adaline SDKs or API.
Monitor and analyze your AI agent’s quality, performance, and usage in real-time by viewing traces, spans, and charts.
Adaline automatically enriches your logs with token usage, cost, evaluation scores, etc.
Easily filter and search logs to identify issues and trends, add them to datasets and use them to improve your prompts.Follow these guides after your AI agent is sending logs to Adaline:
Build, test, and refine prompts in a collaborative, multi-modal playground with version history.
Use the Playground to iterate on your prompts, play with model parameters, test different use cases, and compare results.
Directly investigate and re-play any end-user request from logs to Playground, reproduce the issue, and iterate on a fix.Follow these guides to get hands-on with prompts in Adaline:
Run your prompt against thousands of test cases in Cloud and quantify results with evaluators.
Run and compare evaluations to measure quality and performance, identify regression, and detect drifts.Follow these guides to get hands-on with evaluations in Adaline:
Deploy prompts to your AI applications in real time with isolated environments and versioned deployments.
Track and version your prompt changes over time.
Integrate with your CI/CD pipeline to ship safely and automatically.Follow these guides to get hands-on with deployments in Adaline:
