Building AI agents with coding assistants: the practical FAQ
Answers to common questions about using coding agents to instrument, debug, evaluate, and improve production AI applications safely.
Coding assistants can scaffold an agent quickly. The hard part begins when that agent meets real users, real tools, and real failure modes.
This FAQ covers the production side: instrumentation, evaluation, controlled changes, and how the Currai skill fits into an existing application.
Can a coding assistant connect an existing agent to Currai?
Yes. The Currai skill can inspect a TypeScript, JavaScript, or Python project, locate the real chat or agent path, add dependency-free native HTTP capture, and verify a real interaction.
It does not install a first-party Currai SDK. If the project already emits OpenTelemetry, the coding assistant should preserve that instrumentation and route compatible telemetry instead of creating a duplicate path.
What should be captured?
Capture a stable session identifier plus the events needed to explain the run: model calls, tools, retrievers, guardrails, evaluators, errors, user identity, and relevant metadata.
The goal is not maximum payload volume. The goal is enough connected evidence to reconstruct what the user experienced and why.
Can it instrument both chat and voice agents?
Yes. The application boundary differs, but the evidence model is similar. One conversation or call should retain a stable session identity while model, tool, and evaluation activity attaches to that session.
For streaming applications, telemetry must remain best-effort and must never break or delay the user response if the analytics destination is unavailable.
Can a coding assistant edit prompts safely?
It can propose and implement narrow changes, but meaningful prompt edits should be grounded in evidence. Give the assistant the failure pattern, representative sessions, expected behavior, and evaluation criteria.
Ask it to show the diff and avoid unrelated rewrites. Large prompt changes make regressions harder to diagnose.
What about secrets?
Keep ingestion credentials in the application's existing environment mechanism. Never place keys in source files, prompts, browser bundles, fixtures, or terminal output. Use ingestion-only credentials and avoid giving an agent broader access than the task requires.
How should the integration be verified?
A build is not enough. Run one real interaction through the actual product path, then confirm the session and its events appear in Currai. Verify identifiers, ordering, tool evidence, and failure behavior when capture is unavailable.
Can it turn production failures into evals?
Yes. Repeated, costly, or high-risk failures make strong regression cases. Preserve the user intent and relevant context, remove unnecessary personal data, define a narrow rubric, and run the case against future prompt and model changes.
Will it automatically deploy changes?
Only if you explicitly authorize that workflow. Instrumentation, local verification, deployment, and production monitoring are separate steps. Keeping those boundaries clear makes review and rollback safer.
Start with the Currai skill
Open the Currai skill guide and ask your coding assistant to connect the real AI application path, then verify one genuine interaction.
Or create a free Currai account and generate ingestion credentials from Workspace Settings.