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docs: replace examples that overlapped with flue's README
The previous Issue Triage / Daytona Coding / GitHub MCP / approved-comments
schema sections mirrored flue's README too closely. Replaced with examples
that are differentiated and lean into Rust-specific strengths:
- Snapshot Repair (CI): cargo-test-fail loop, blesses safe insta snapshots.
- Codebase Cartographer (Parallel Tasks): fan out one detached Session::task
per module to produce ARCHITECTURE.md.
- Reproducer Sandbox (Remote Linux): focused HttpSessionEnv use "reproduce
this Linux-only failure" instead of generic "deploy a coding agent."
- MCP Tools (Sentry): swapped from GitHub MCP to a Sentry workflow.
- Schema-Guided Cargo Audit: typed advisories/risk/next_action result.
Quickstart and Coding Agent (Local Repo) kept as-is — neither overlaps.
The harness writes a run-scoped brief to `.agentic-harness/runs/<id>/coding-brief.md`, streams progress, captures the agent result, and saves a structured summary at `.agentic-harness/runs/latest.{md,json}` so a person or another agent can read what happened.
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### Issue Triage (CI)
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### Snapshot Repair (CI)
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A CLI-only agent that runs in CI when an issue is opened. No HTTP trigger — just `agentic-harness run triage`. Skills and roles in the workspace shape behavior; secrets stay in env.
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A CLI-only agent that runs in CI after `cargo test` produces failing `*.snap.new` files. It reads the diffs, decides which are safe to bless under a workspace policy (additive output, ordering changes, whitespace), applies the safe ones, and flags the rest for human review. No HTTP trigger.
Agentic Harness's deployment primitive for coding agents: the agent stays native Rust; shell and file operations run inside a remote Linux sandbox over a small HTTP protocol. Configure once with `setup sandbox`, then any session can target it via `HttpSessionEnv`.
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A one-shot agent that produces `ARCHITECTURE.md` for a repo it's never seen. It fans out one detached `Session::task` per top-level module, each with its own message history but sharing the workspace, then merges the children's notes into a single document. This is the Rust analogue of "kick off N research subagents in parallel and stitch the results."
Each child task gets a fresh `AGENTS.md` + skill discovery scoped to its working directory, so adding a `module-summarizer` role tunes every task at once.
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### Reproducer Sandbox (Remote Linux)
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When an issue says "this fails on Linux but I'm on macOS," the agent provisions a clean Linux sandbox over `HttpSessionEnv`, checks out the branch, runs the reproducer steps, and captures evidence. The agent stays a native Rust binary on your laptop; shell and file operations run on the other side of an HTTP boundary.
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```rust
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useagentic_harness::HttpSessionEnv;
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letremote=HttpSessionEnv::new(
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"https://sandbox.example/session",
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letsandbox=HttpSessionEnv::new(
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std::env::var("SANDBOX_URL")?, // Vercel Sandbox / Daytona / E2B / your own
The same protocol is documented in [`docs/http-session-env.md`](docs/http-session-env.md) — any sandbox provider that speaks it works without a custom adapter. The CLI surfaces it for ad-hoc use too:
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```bash
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agentic-harness setup sandbox --target daytona --print | claude
agentic-harness sandbox exec"uname -a && rustc --version" --json
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```
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When `.agentic-harness/sandbox.toml` points at a remote endpoint, `agentic-harness code` syncs the workspace into the sandbox and runs checks there instead of locally.
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### MCP Tools (Sentry)
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### MCP Tools
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MCP servers plug in as runtime tool providers. Streamable HTTP by default; pass `transport: Sse` for legacy SSE servers.
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MCP servers plug in as runtime tool providers. Connect once, hand the tools to a session, and the model can call `find_event`, `list_issues`, etc. directly. Streamable HTTP by default; pass `transport: Sse` for legacy SSE servers.
"Find the highest-volume new error in the last 24h, locate the commit that introduced it, \
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and draft a hot-fix plan with rollback steps.",
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)?;
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```
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### Schema-Guided Results
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### Schema-Guided Cargo Audit
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Get typed, schema-validated data back from a prompt without manual JSON wrangling.
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Get typed, schema-validated data back from a prompt without manual JSON wrangling. The model returns prose plus a structured block; `prompt_json_with_options` extracts and decodes it directly into your type.
Structured `---RESULT_START---` / `---RESULT_END---` block extraction is built in, so the model can return prose plus a typed payload.
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Structured `---RESULT_START---` / `---RESULT_END---` block extraction is built in, so the model can return reasoning prose alongside the typed payload.
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