Deploy Agents.
Not Just Models.
Traditional MLOps isn't enough. You need a CI/CD pipeline built for autonomous agents—handling non-determinism, tool drift, and multi-step reasoning evaluations.
Why "Standard" DevOps Fails AI
Deploying code is deterministic. Deploying agents is probabilistic. A slight prompt change or model update can break complex workflows in subtle ways.
Without specialized evaluation frameworks (LLM-as-a-Judge) and tracing tools, you are flying blind. You won't know your agent is hallucinating until a customer complains.
Continuous Delivery for Intelligence
We build robust pipelines that test, monitor, and improve your agents automatically.
Automated Evals
Run thousands of test cases against your agents before every deployment. Use "LLM-as-a-Judge" to score reasoning, tone, and accuracy.
Observability & Tracing
Full visibility into every step of the agent's thought process. Trace tool calls, latency, and token usage with LangSmith or Arize.
Feedback Loops
Capture user feedback (thumbs up/down, corrections) and automatically add it to your fine-tuning dataset to improve the model over time.
The Agent Ops Stack
Evaluation Frameworks
DeepEval or Ragas for rigorous testing of RAG and agent performance.
Tracing & Monitoring
LangSmith, Arize Phoenix, or Weights & Biases for production visibility.
Model Registry
MLflow or Hugging Face Hub to version control your prompts and weights.
Ship Agents with Confidence
Stop guessing. Start engineering. Build a pipeline that ensures quality at scale.