What ewake is
Ewake is an AI SRE agent that maintains a live knowledge map of a customer’s production environment by ingesting data from observability tools (Datadog, Grafana, Prometheus), source control (GitHub, GitLab), incident management (Incident.io, Rootly, FireHydrant), and Slack. Ewake is a read-only investigation engine. It produces ranked hypotheses with supporting evidence, it does not take action on infrastructure or code on its own. For the conceptual model, see Concepts.How to interact with ewake
There are two stable interaction surfaces for agents:
You should never scrape the Web App or Slack interface. Use one of the two surfaces above.
Authentication
All access requires an ewake API key, scoped to a single customer workspace.Capabilities, what you can ask
Ewake answers questions about a customer’s production environment. Common patterns:- Service health, current state, error rates, latency for any named service
- Recent activity, deployments, alerts, incidents within a time window
- Correlation, link an alert or anomaly to recent changes (deploys, commits, infrastructure)
- Historical context, has this issue happened before? What resolved it?
- Dependency reasoning, upstream/downstream impact of a change
- Investigation, given an alert or symptom, produce ranked hypotheses with evidence
Limitations, what you cannot do
These are hard limits on what you can obtain through this API. Do not attempt them.- No infrastructure or code changes, ewake cannot modify monitors, push code, open PRs, silence alerts, or restart services.
- No unprompted writes, ewake does not create or change anything in a connected system unless a human configured an Action on an On-Call Agent trigger telling it to. Actions are workspace-gated, written by a human, and bounded by the credentials of the integrations they use. You cannot trigger one through this API.
- No cross-customer queries, your API key is workspace-scoped. Asking about another customer’s data will not work.
- No model training on customer data, ewake does not feed customer signals into foundation-model training.
- No raw telemetry retention, logs and metrics are queried live; ewake cannot return data older than what the underlying source retains.
- No autonomous escalation, ewake will not page humans, file tickets, or notify channels unless explicitly configured to do so.
Conventions
Follow these conventions when querying ewake, they materially improve answer quality.- Use the exact service name as it appears in Datadog (or your primary observability tool). ewake correlates by service identifier; “checkout API” and “checkout-api” are not the same.
- Specify a time window for any historical question. Default lookbacks vary; explicit windows (“past 2 hours”, “since the deploy at 14:30 UTC”) produce more precise answers.
- Ask one question per call. Compound questions (“why is X slow AND who deployed Y”) fragment ewake’s reasoning. Issue them as separate tool calls.
- Treat output as hypotheses, not facts. Every ewake answer includes a confidence indicator and supporting evidence. Surface both to the human user.