Ever wonder why testing AI agents locally falls apart when tasks multiply?
⚡ TL;DR: Build scalable agent evaluations with Harbor's isolated sandboxes, structured datasets, and LangSmith tracing.
The Simple Version
Testing an agent on your laptop works for a few examples but breaks when you need to run thousands of tasks.🎯 Think of it this way: It's like trying to taste a whole restaurant menu by sampling one bite.
How It Actually Works
Step 1: Create a dataset
You define a JSON file that lists inputs and expected outputs. Each row becomes a separate test case.Step 2: Build a sandbox
Harbor runs each agent call in its own isolated environment so resources don't clash.Step 3: Run evaluations
The CLI command harbor run executes the agent against every dataset entry in parallel.Step 4: Observe with LangSmith
Results appear in LangSmith datasets where you can filter, score, and trace each execution.Technical Deep Dive
You install Harbor via Docker, set the HARBOR_API_KEY, and mount a local directory as a dataset. The agent code uses the OpenAI SDK to read and edit a CSV file. Harbor injects a unique sandbox ID, runs the agent, captures logs, and writes a result row back to the dataset. LangSmith ingests these rows, links them to traces, and lets you compute scores programmatically.Why This Matters
Scalable evaluations let you iterate on prompts, tools, and models without manual re-testing. Teams can automate performance regressions and catch bugs before they hit production. The combination of isolation and observability turns chaotic experiments into repeatable, data-driven pipelines.Key Takeaways
- Local testing is fine for prototypes but not for production scale.
- Harbor provides sandbox isolation and dataset versioning.
- LangSmith offers full traceability and scoring.
- Running evaluations in parallel speeds up iteration.
- You can automate regression checks in CI pipelines.
- Start small: define a CSV dataset and a simple agent.
- Expand to more complex tasks as your pipeline matures.