Telemetry Ethics For Open Source Assistants Operational Checklist: practical systems guide

Shipping an open-source coding assistant means grappling with a unique ethical tension: you need usage data to improve the product, but your users chose you *because* you respect their autonomy. Get telemetry wrong and you've burned trust with the only community that matters. This checklist walks through the architectural and operational decisions that let you collect what you need without becoming what you despise.

Default to zero collection

The cleanest position is no telemetry at all until a user explicitly opts in. This means:

• Your binary or container ships with telemetry disabled in the default config
• First-run prompts ask permission before any network call
• The opt-in moment explains *exactly* what data you collect and why

If you're building something like Goatfied's agent loop where the system plans, constrains, edits, validates, and retries code changes, you probably want to know which validation failures are common and which constraint violations users hit most often. Frame that need honestly: "We collect failed lint rule names (not your code) to improve our constraint library. Opt in?" is defensible. Silent background collection is not.

Self-hosted deployments make this easier. If a team runs their own Goatfied instance behind their firewall, they control the telemetry endpoint. You can ship an open telemetry collector config they can route to their own observability stack, or they can point it at `/dev/null`. The SaaS-only model forces you to be even more surgical about what leaves their environment.

Separate operational health from product analytics

Not all telemetry serves the same purpose. Split it into two streams:

**Operational health**: crash dumps, error rates, latency percentiles, resource usage. This keeps your service running and helps users debug their own deployments. Structure these as structured logs or metrics (Prometheus, StatsD) that contain no user content and minimal context.

**Product analytics**: which features get used, where users abandon flows, what constraint rules fire most often. This informs roadmap decisions. It's also where you'll be tempted to over-collect.

Run them through different pipelines with different retention policies. Operational metrics might live 90 days; product events could be anonymized and aggregated immediately, with raw data dropped after 7 days. Document both policies in your repository's `TELEMETRY.md` and honor them in code, not just in prose.

Make telemetry code auditable

If you claim your telemetry respects privacy, the code that collects and ships it must be trivially auditable. This means:

• All telemetry calls go through a single facade or client in your codebase (e.g., `telemetry.track_event()`, never scattered fetch calls)
• That module has a short, readable implementation
• The schema of every event type is defined in one file, versioned, and includes comments explaining why you collect each field
• You include a CLI flag like `--telemetry-dry-run` that prints events to stdout instead of shipping them

When a user can run `goatfied --telemetry-dry-run edit my_file.py` and see exactly what JSON blobs would leave their machine, you've eliminated the guesswork. They don't have to trust your marketing page; they can read the output and decide.

Strip identifying content by default

Even when a user opts in, treat their code as radioactive. A well-designed event schema for a coding assistant might include:

• File extension (`.py`, `.ts`, `.go`)
• Byte count or line count
• Lint rule names that failed
• Time to compile/validate
• Whether the agent loop succeeded or retried

It should *never* include:

• Actual code snippets (unless the user explicitly opts into a "share anonymized diffs for model training" tier, with separate consent)
• File paths containing customer names, project names, or internal structure
• Variable or function names
• Stack traces with source line content

If you need stack traces for crash debugging, hash filenames and strip line contents, or require a second explicit opt-in for "verbose diagnostics."

Goatfied's compile-first validation loop is a good forcing function here: you can count how many times `cargo check` or `tsc` fails without ever sending the compiler's output off-machine. The exit code and timing are enough to spot patterns.

Offer local-only analytics modes

Some teams want dashboards without data exfiltration. Provide a mode where telemetry is written to a local SQLite or JSON-lines file. Ship a small dashboard (or Grafana config) that reads from it.

This works especially well for self-hosted deployments. A company can point their Goatfied fleet at an internal metrics aggregator, run their own analytics, and never let event data cross the boundary. You lose visibility, but you gain credibility. Users who see you support this path are more likely to trust the managed offering.

Version and deprecate your telemetry schema

Treat your telemetry schema like a public API. When you add a field or change an event structure, bump a version number and document the change in your release notes. When you remove a field, announce it a version ahead and log warnings if old clients still emit it.

This discipline matters for two reasons. First, it makes audits easier: a security team reviewing your tool can diff schema versions and see exactly what changed. Second, it forces you to think twice before collecting new data. If you have to write a changelog entry saying "We now collect the full list of LSP servers attached to each session," you might reconsider whether you need that.

Publish aggregated insights, not raw data

If you claim to use telemetry to improve the product, show your work. Publish quarterly summaries: "42% of compile failures in the agent loop were due to missing imports; we shipped an improved import suggester." Give percentages, trends, and anonymized category breakdowns.

This serves two purposes: it proves the data is actually useful, and it demonstrates you're aggregating and anonymizing properly. If you can't publish insights without leaking sensitive details, your collection is probably too granular.

Build a kill switch and honor it immediately

Every telemetry client should check a boolean flag on startup and a cached remote config at intervals. If that flag flips to "disable," all telemetry stops until the next explicit opt-in. No grace period, no queued events, no "we'll flush what's in memory first."

This is your escape hatch when you discover you've been collecting something you shouldn't. A user reports "your telemetry accidentally includes my org's internal ticket IDs in error messages"? Flip the switch, fix the schema, re-request consent. The ability to do this cleanly is the difference between a contained incident and a PR disaster.

Related reading

• [Goatfied vs Cursor vs GitHub Copilot: a benchmark across 50 real PR tasks](/blog/goatfied-vs-cursor-vs-github-copilot-benchmark-50-pr-tasks)
• [The Goatfied agent loop: how we ship code that actually compiles first try](/blog/goatfied-agent-loop-compiles-first-try)