Here’s a simple, high‑leverage UX metric to add to your pipeline run view that will immediately make DevOps feel faster and calmer:

# Time‑to‑First‑Signal (TTFS)

**What it is:** the time from opening a run’s details page until the UI renders the **first actionable insight** (e.g., “Stage `build` failed – `dotnet restore` 401 – token expired”).
**Why it matters:** engineers don’t need *all* data instantly—just the first trustworthy clue to start acting. Lower TTFS = quicker triage, lower stress, tighter MTTR.

---

## What counts as a “first signal”

* Failed stage + reason (exit code, key log line, failing test name)
* Degraded but actionable status (e.g., flaky test signature)
* Policy gate block with the specific rule that failed
* Reachability‑aware security finding that blocks deploy (one concrete example, not the whole list)

> Not a signal: spinners, generic “loading…”, or unactionable counts.

---

## How to optimize TTFS (practical steps)

1. **Deferred loading (prioritize critical panes):**

   * Render header + failing stage card first; lazy‑load artifacts, full logs, and graphs after.
   * Pre‑expand the *first failing node* in the stage graph.

2. **Log pre‑indexing at ingest:**

   * During CI, stream logs into chunks keyed by `[jobId, phase, severity, firstErrorLine]`.
   * Extract the **first error tuple** (timestamp, step, message) and store it next to the job record.
   * On UI open, fetch only that tuple (sub‑100 ms) before fetching the rest.

3. **Cached summaries:**

   * Persist a tiny JSON “run.summary.v1” (status, first failing stage, first error line, blocking policies) in Redis/Postgres.
   * Invalidate on new job events; always serve this summary first.

4. **Edge prefetch:**

   * When the runs table is visible, prefetch summaries for rows in viewport so details pages open “warm”.

5. **Compress + cap first log burst:**

   * Send the first **5–10 error lines** (already extracted) immediately; stream the rest.

---

## Instrumentation (so you can prove it)

Emit these points as telemetry:

* `ttfs_start`: when the run details route is entered (or when tab becomes visible)
* `ttfs_signal_rendered`: when the first actionable card is in the DOM
* `ttfs_ms = signal_rendered - start`
* Dimensions: `pipeline_provider`, `repo`, `branch`, `run_type` (PR/main), `device`, `release`, `network_state`

**SLO:** *P50 ≤ 700 ms, P95 ≤ 2.5 s* (adjust to your infra).

**Dashboards to track:**

* TTFS distribution (P50/P90/P95) by release
* Correlate TTFS with bounce rate and “open → rerun” delay
* Error budget: % of views with TTFS > 3 s

---

## Minimal backend contract (example)

```json
GET /api/runs/{runId}/first-signal
{
  "runId": "123",
  "firstSignal": {
    "type": "stage_failed",
    "stage": "build",
    "step": "dotnet restore",
    "message": "401 Unauthorized: token expired",
    "at": "2025-12-11T09:22:31Z",
    "artifact": { "kind": "log", "range": {"start": 1880, "end": 1896} }
  },
  "summaryEtag": "W/\"a1b2c3\""
}
```

---

## Frontend pattern (Angular 17, signal‑first)

* Fire `first-signal` request in route resolver.
* Render `FirstSignalCard` immediately.
* Lazy‑load stage graph, full logs, security panes.
* Fire `ttfs_signal_rendered` when `FirstSignalCard` enters viewport.

---

## CI adapter hints (GitLab/GitHub/Azure)

* Hook on job status webhooks to compute & store the first error tuple.
* For GitLab: scan `trace` stream for first `ERRO|FATAL|##[error]` match; store to DB table `ci_run_first_signal(run_id, stage, step, message, t)`.

---

## “Good TTFS” acceptance tests

* Run with early fail → first signal < 1 s, shows exact command + exit code.
* Run with policy gate fail → rule name + fix hint visible first.
* Offline/slow network → cached summary still renders an actionable hint.

---

## Copy to put in your UX guidelines

> “Optimize **Time‑to‑First‑Signal (TTFS)** above all. Users must see one trustworthy, actionable clue within 1 second on a warm path—even if the rest of the UI is still loading.”

If you want, I can sketch the exact DB schema for the pre‑indexed log tuples and the Angular resolver + telemetry hooks next.
Below is an extended, end‑to‑end implementation plan for **Time‑to‑First‑Signal (TTFS)** that you can drop into your backlog. It includes architecture, data model, API contracts, frontend work, observability, QA, and rollout—structured as epics/phases with “definition of done” and acceptance criteria.

---

# Scope extension

## What we’re building

A run details experience that renders **one actionable clue** fast—before loading heavy UI like full logs, graphs, artifacts.

**“First signal”** is a small payload derived from run/job events and the earliest meaningful error evidence (stage/step + key log line(s) + reason/classification).

## What we’re extending beyond the initial idea

1. **First‑Signal Quality** (not just speed)

   * Classify error type (auth, dependency, compilation, test, infra, policy, timeout).
   * Identify “culprit step” and a stable “signature” for dedupe and search.
2. **Progressive disclosure UX**

   * Summary → First signal card → expanded context (stage graph, logs, artifacts).
3. **Provider‑agnostic ingestion**

   * Adapters for GitLab/GitHub/Azure (or your CI provider).
4. **Caching + prefetch**

   * Warm open from list/table, with ETags and stale‑while‑revalidate.
5. **Observability & SLOs**

   * TTFS metrics, dashboards, alerting, and quality metrics (false signals).
6. **Rollout safety**

   * Feature flags, canary, A/B gating, and a guaranteed fallback path.

---

# Success criteria

## Primary metric

* **TTFS (ms)**: time from details page route enter → first actionable signal rendered.

## Targets (example SLOs)

* **P50 ≤ 700 ms**, **P95 ≤ 2500 ms** on warm path.
* **Cold path**: P95 ≤ 4000 ms (depends on infra).

## Secondary outcome metrics

* **Open→Action time**: time from opening run to first user action (rerun, cancel, assign, open failing log line).
* **Bounce rate**: close page within 10 seconds without interaction.
* **MTTR proxy**: time from failure to first rerun or fix commit.

## Quality metrics

* **Signal availability rate**: % of run views that show a first signal card within 3s.
* **Signal accuracy score** (sampled): engineer confirms “helpful vs not”.
* **Extractor failure rate**: parsing errors / missing mappings / timeouts.

---

# Architecture overview

## Data flow

1. **CI provider events** (job started, job finished, stage failed, log appended) land in your backend.
2. **Run summarizer** maintains:

   * `run_summary` (small JSON)
   * `first_signal` (small, actionable payload)
3. **UI opens run details**

   * Immediately calls `GET /runs/{id}/first-signal` (or `/summary`).
   * Renders FirstSignalCard as soon as payload arrives.
4. Background fetches:

   * Stage graph, full logs, artifacts, security scans, trends.

## Key decision: where to compute first signal

* **Option A: at ingest time (recommended)**
  Compute first signal when logs/events arrive, store it, serve it instantly.
* **Option B: on demand**
  Compute when user opens run details (simpler initially, worse TTFS and load).

---

# Data model

## Tables (relational example)

### `ci_run`

* `run_id (pk)`
* `provider`
* `repo_id`
* `branch`
* `status`
* `created_at`, `updated_at`

### `ci_job`

* `job_id (pk)`
* `run_id (fk)`
* `stage_name`
* `job_name`
* `status`
* `started_at`, `finished_at`

### `ci_log_chunk`

* `chunk_id (pk)`
* `job_id (fk)`
* `seq` (monotonic)
* `byte_start`, `byte_end` (range into blob)
* `first_error_line_no` (nullable)
* `first_error_excerpt` (nullable, short)
* `severity_max` (info/warn/error)

### `ci_run_summary`

* `run_id (pk)`
* `version` (e.g., `1`)
* `etag` (hash)
* `summary_json` (small, 1–5 KB)
* `updated_at`

### `ci_first_signal`

* `run_id (pk)`
* `etag`
* `signal_json` (small, 0.5–2 KB)
* `quality_flags` (bitmask or json)
* `updated_at`

## Cache layer

* Redis keys:

  * `run:{runId}:summary:v1`
  * `run:{runId}:first-signal:v1`
* TTL: generous but safe (e.g., 24h) with “write‑through” on event updates.

---

# First signal definition

## `FirstSignal` object (recommended shape)

```json
{
  "runId": "123",
  "computedAt": "2025-12-12T09:22:31Z",
  "status": "failed",
  "firstSignal": {
    "type": "stage_failed",
    "classification": "dependency_auth",
    "stage": "build",
    "job": "build-linux-x64",
    "step": "dotnet restore",
    "message": "401 Unauthorized: token expired",
    "signature": "dotnet-restore-401-unauthorized",
    "log": {
      "jobId": "job-789",
      "lines": [
        "error : Response status code does not indicate success: 401 (Unauthorized).",
        "error : The token is expired."
      ],
      "range": { "start": 1880, "end": 1896 }
    },
    "suggestedActions": [
      { "label": "Rotate token", "type": "doc", "target": "internal://docs/tokens" },
      { "label": "Rerun job", "type": "action", "target": "rerun-job:job-789" }
    ]
  },
  "etag": "W/\"a1b2c3\""
}
```

### Notes

* `signature` should be stable for grouping.
* `suggestedActions` is optional but hugely valuable (even 1–2 actions).

---

# APIs

## 1) First signal endpoint

**GET** `/api/runs/{runId}/first-signal`

Headers:

* `If-None-Match: W/"..."` supported
* Response includes `ETag` and `Cache-Control`

Responses:

* `200`: full first signal object
* `304`: not modified
* `404`: run not found
* `204`: run exists but signal not available yet (rare; should degrade gracefully)

## 2) Summary endpoint (optional but useful)

**GET** `/api/runs/{runId}/summary`

* Includes: status, first failing stage/job, timestamps, blocking policies, artifact counts.

## 3) SSE / WebSocket updates (nice-to-have)

**GET** `/api/runs/{runId}/events` (SSE)

* Push new signal or summary updates in near real-time while user is on the page.

---

# Frontend implementation plan (Angular 17)

## UX behavior

1. **Route enter**

   * Start TTFS timer.
2. Render instantly:

   * Title, status badge, pipeline metadata (run id, commit, branch).
   * Skeleton for details area.
3. Fetch first signal:

   * Render `FirstSignalCard` immediately when available.
   * Fire telemetry event when card is **in DOM and visible**.
4. Lazy-load:

   * Stage graph
   * Full logs viewer
   * Artifacts list
   * Security findings
   * Trends, flaky tests, etc.

## Angular structure

* `RunDetailsResolver` (or `resolveFn`) requests first signal.
* `RunDetailsComponent` uses signals to render quickly.
* `FirstSignalCardComponent` is standalone + minimal deps.

## Prefetch strategy from runs list view

* When the runs table is visible, prefetch summaries/first signals for items in viewport:

  * Use `IntersectionObserver` to prefetch only visible rows.
  * Store results in an in-memory cache (e.g., `Map<runId, FirstSignal>`).
  * Respect ETag to avoid redundant payloads.

## Telemetry hooks

* `ttfs_start`: route activation + tab visible
* `ttfs_signal_rendered`: FirstSignalCard attached and visible
* Dimensions: provider, repo, branch, run_type, release_version, network_state

---

# Backend implementation plan

## Summarizer / First-signal service

A service or module that:

* subscribes to run/job events
* receives log chunks (or pointers)
* computes and stores:

  * `run_summary`
  * `first_signal`
* publishes updates (optional) to an event stream for SSE

### Concurrency rule

First signal should be set once per run unless a “better” signal appears:

* if current signal is missing → set
* if current signal is “generic” and new one is “specific” → replace
* otherwise keep (avoid churn)

---

# Extraction & classification logic

## Minimum viable extractor (Phase 1)

* Heuristics:

  * first match among patterns: `FATAL`, `ERROR`, `##[error]`, `panic:`, `Unhandled exception`, `npm ERR!`, `BUILD FAILED`, etc.
  * plus provider-specific fail markers
* Pull:

  * stage/job/step context (from job metadata or step boundaries)
  * 5–10 log lines around first error line

## Improved extractor (Phase 2+)

* Language/tool specific rules:

  * dotnet, maven/gradle, npm/yarn/pnpm, python/pytest, go test, docker build, terraform, helm
* Add `classification` and `signature`:

  * normalize common errors:

    * auth expired/forbidden
    * missing dependency / DNS / TLS
    * compilation error
    * test failure (include test name)
    * infra capacity / agent lost
    * policy gate failure

## Guardrails

* **Secret redaction**: before storing excerpts, run your existing redaction pipeline.
* **Payload cap**: cap message length and excerpt lines.
* **PII discipline**: avoid including arbitrary stack traces if they contain sensitive paths; include only key lines.

---

# Development plan by phases (epics)

Each phase below includes deliverables + acceptance criteria. You can treat each as a sprint/iteration.

---

## Phase 0 — Baseline and alignment

### Deliverables

* Baseline TTFS measurement (current behavior)
* Definition of “actionable signal” and priority rules
* Performance budget for run details view

### Tasks

* Add client-side telemetry for current page load steps:

  * route enter, summary loaded, logs loaded, graph loaded
* Measure TTFS proxy today (likely “time to status shown”)
* Identify top 20 failure modes in your CI (from historical logs)

### Acceptance criteria

* Dashboard shows baseline P50/P95 for current experience.
* “First signal” contract signed off with UI + backend teams.

---

## Phase 1 — Data model and storage

### Deliverables

* DB migrations for `ci_run_summary` and `ci_first_signal`
* Redis cache keys and invalidation strategy
* ADR: where summaries live and how they update

### Tasks

* Create tables and indices:

  * index on `run_id`, `updated_at`, `provider`
* Add serializer/deserializer for `summary_json` and `signal_json`
* Implement ETag generation (hash of JSON payload)

### Acceptance criteria

* Can store and retrieve summary + first signal for a run in < 50ms (DB) and < 10ms (cache).
* ETag works end-to-end.

---

## Phase 2 — Ingestion and first signal computation

### Deliverables

* First-signal computation module
* Provider adapter integration points (webhook consumers)
* “first error tuple” extraction from logs

### Tasks

* On job log append:

  * scan incrementally for first error markers
  * store excerpt + line range + job/stage/step mapping
* On job finish/fail:

  * finalize first signal with best known context
* Implement the “better signal replaces generic” rule

### Acceptance criteria

* For a known failing run, API returns first signal without reading full log blob.
* Computation does not exceed a small CPU budget per log chunk (guard with limits).
* Extraction failure rate < 1% for sampled runs (initial).

---

## Phase 3 — API endpoints and caching

### Deliverables

* `/runs/{id}/first-signal` endpoint
* Optional `/runs/{id}/summary`
* Cache-control + ETag support
* Access control checks consistent with existing run authorization

### Tasks

* Serve cached first signal first; fallback to DB
* If missing:

  * return `204` (or a “pending” object) and allow UI fallback
* Add server-side metrics:

  * endpoint latency, cache hit rate, payload size

### Acceptance criteria

* Endpoint P95 latency meets target (e.g., < 200ms internal).
* Cache hit rate is high for active runs (after prefetch).

---

## Phase 4 — Frontend progressive rendering

### Deliverables

* FirstSignalCard component
* Route resolver + local cache
* Prefetch on runs list view
* Telemetry for TTFS

### Tasks

* Render shell immediately
* Fetch and render first signal
* Lazy-load heavy panels using `@defer` / dynamic imports
* Implement “open failing stage” default behavior

### Acceptance criteria

* In throttled network test, first signal card appears significantly earlier than logs and graphs.
* `ttfs_signal_rendered` fires exactly once per view, with correct dimensions.

---

## Phase 5 — Observability, dashboards, and alerting

### Deliverables

* TTFS dashboards by:

  * provider, repo, run type, release version
* Alerts:

  * P95 regression threshold
* Quality dashboard:

  * availability rate, extraction failures, “generic signal rate”

### Tasks

* Create event pipeline for telemetry into your analytics system
* Define SLO/error budget alerts
* Add tracing (OpenTelemetry) for endpoint and summarizer

### Acceptance criteria

* You can correlate TTFS with:

  * bounce rate
  * open→action time
* You can pinpoint whether regressions are backend, frontend, or provider‑specific.

---

## Phase 6 — QA, performance testing, rollout

### Deliverables

* Automated tests
* Feature flag + gradual rollout
* A/B experiment (optional)

### Tasks

**Testing**

* Unit tests:

  * extractor patterns
  * classification rules
* Integration tests:

  * simulated job logs with known outcomes
* E2E (Playwright/Cypress):

  * verify first signal appears before logs
  * verify fallback path works if endpoint fails
* Performance tests:

  * cold cache vs warm cache
  * throttled CPU/network profiles

**Rollout**

* Feature flag:

  * enabled for internal users first
  * ramp by repo or percentage
* Monitor key metrics during ramp:

  * TTFS P95
  * API error rate
  * UI error rate
  * cache miss spikes

### Acceptance criteria

* No increase in overall error rates.
* TTFS improves at least X% for a meaningful slice of users (define X from baseline).
* Fallback UX remains usable when signals are unavailable.

---

# Backlog examples (ready-to-create Jira tickets)

## Epic: Run summary and first signal storage

* Create `ci_first_signal` table
* Create `ci_run_summary` table
* Implement ETag hashing
* Implement Redis caching layer
* Add admin/debug endpoint (internal only) to inspect computed signals

## Epic: Log chunk extraction

* Implement incremental log scanning
* Store first error excerpt + range
* Map excerpt to job + step
* Add redaction pass to excerpts

## Epic: Run details progressive UI

* FirstSignalCard UI component
* Lazy-load logs viewer
* Default to opening failing stage
* Prefetch signals in runs list

## Epic: Telemetry and dashboards

* Add `ttfs_start` and `ttfs_signal_rendered`
* Add endpoint latency metrics
* Build dashboards + alerts
* Add sampling for “signal helpfulness” feedback

---

# Risk register and mitigations

## Risk: First signal is wrong/misleading

* Mitigation:

  * track “generic signal rate” and “corrected by user” feedback
  * classification confidence scoring
  * always provide quick access to full logs as fallback

## Risk: Logs contain secrets

* Mitigation:

  * redact excerpts before storing/serving
  * cap excerpt lines and length
  * keep raw logs behind existing permissions

## Risk: Increased ingest CPU cost

* Mitigation:

  * incremental scanning with early stop after first error captured
  * limit scanning per chunk
  * sample/skip overly large logs until job completion

## Risk: Cache invalidation bugs

* Mitigation:

  * ETag-based correctness
  * versioned keys: `:v1`
  * “write-through” cache updates from summarizer

---

# Definition of Done checklist

A phase is “done” when:

* ✅ TTFS measured with reliable client events
* ✅ FirstSignalCard renders from `/first-signal` endpoint
* ✅ ETag caching works
* ✅ Fallback path is solid (no blank screens)
* ✅ Dashboards exist and are actively watched during rollout
* ✅ Security review completed for log excerpts/redaction
* ✅ Load tests show no unacceptable backend regressions

---

# Optional enhancements after initial launch

1. **Next-step recommendations**
   Add action suggestions and deep links (rotate token, open failing test, open doc).
2. **Flaky test / known issue detection**
   Show “this matches known flaky signature” with last-seen frequency.
3. **“Compare to last green”**
   Summarize what changed since last successful run (commit diff, dependency bump).
4. **SSE live updates**
   Update first signal as soon as failure occurs while user watches.

---

If you tell me your current backend stack (Node/Go/.NET), log storage (S3/Elastic/Loki), and which CI providers you support, I can translate this into a concrete set of modules/classes, exact schema migrations, and the Angular routing + signals code structure you’d implement.