Use cases
Engineering & SRE

Reconstruct yesterday's incident

PagerDuty + #incidents + meeting transcript + git history, woven into one timeline.

Best for
Post-mortem prep
Primitives
  • Interactions
  • Events
  • Time
Side by side

Token Usage, with and without Sentra

~45k tokens saved60% less work for the agent

Without Sentra
~75k tokens
With Sentra
~30k tokens
Claude (without Sentra)
  1. Pull PagerDuty events in the incident window. Parse alert text and acknowledgments.
  2. Pull the #incidents Slack channel history for the window. Read every message to identify the material ones.
  3. Identify the meeting that ran during the incident — calendar lookup, transcript pull.
  4. Pull the post-mortem doc draft if it exists.
  5. Pull git push and deploy events from CI logs.
  6. Manually order every event by timestamp. Label source, actor, what happened.
  7. Re-read responder threads to extract converged root-cause hypothesis.
~75k tokens
Claude + Sentra
  1. Pull every Interaction in the incident window across PagerDuty, Slack, meetings, docs, and git.
  2. Already ordered by timestamp, with source, Actor, and event content typed.
  3. Decision primitives extracted from responder threads — root cause hypothesis is structured.
  4. Compose the timeline table and the first-draft Root Cause Hypothesis.
~30k tokens
Agent prompt
You are reconstructing the timeline of incident {{incident_id_or_date}}.

Using Sentra:

1. Pull relevant data within the incident window (15 minutes before first page → 30 minutes after resolution) across PagerDuty, the #incidents Slack channel, any meeting that ran during the window, the post-mortem doc draft, and git push / deploy events.
2. Order strictly by timestamp.
3. For each row, label: source, actor, what happened in one sentence, and (if applicable) the Decision that was made at that moment.

Output:

- A timeline table: time · source · actor · event · decision (if any).
- Three "key moments" with the quoted Interaction excerpt.
- A first-draft Root Cause Hypothesis paragraph derived from the converged Rationale Sentra extracted from the responders.

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Subprocessors include Amazon Web Services, GitHub, Slack, Google Cloud Platform, and OpenAI.

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