- Resilience patterns implemented in your services
- Observability stack configuration — traces, metrics, logs
- On-call runbook
Resilience & Observability
Systems that fail gracefully, and tell you why.
Resilience isn't “it doesn't go down” — it's “when part of it does go down, the rest keeps working, and you find out why in minutes, not hours”.
We implement circuit breakers, bulkheads and retries with sane backoff and timeouts, plus distributed tracing, metrics and structured logging, so a production incident comes with an answer attached.
01 In practice
What this looks like in practice
01
Circuit breakers and bulkheads placed at your actual failure boundaries, not everywhere generically.
02
Retry policies with backoff tuned to the downstream system's actual recovery behaviour.
03
Distributed tracing across service boundaries, so a slow request has a story, not just a timestamp.
04
Structured logging and metrics that answer “what changed” before someone has to ask.
05
On-call runbooks written from real incident scenarios, not hypothetical ones.
02 Deliverables
What you get
03 FAQ
Resilience & observability — frequently asked questions
What does resilience actually mean here?
Not “it doesn't go down”. It means that when part of it does go down, the rest keeps working, and you find out why in minutes rather than hours. Chasing zero failure is a losing game in a distributed system; designing for graceful degradation and fast diagnosis is not.
Where should circuit breakers go?
At your actual failure boundaries — the calls that cross a process, a network or a vendor — rather than generically on everything. Breakers applied indiscriminately add latency and failure modes of their own while obscuring where the real boundaries are. The useful question is which dependencies can fail independently, and what the system should do when each one does.
Why isn't a retry with backoff enough on its own?
Because retries alone amplify an outage. If a downstream system is struggling and every caller retries, you have built a load generator pointed at the thing you are trying to protect — this is how a retry storm takes down a service that would otherwise have recovered. Backoff has to be tuned to the downstream system's real recovery behaviour, and paired with a breaker that stops calling entirely when it is clearly unhealthy.
What does distributed tracing give us that logs don't?
Causality across service boundaries. Logs tell you what each service did; a trace tells you that this request went through these seven hops and spent 4 seconds in the third one. Without it, a slow request is a timestamp and a guess. With it, it has a story, and the failing hop is found in minutes rather than reconstructed by hand from separate log streams.
Do you write the on-call runbooks too?
Yes, and from real incident scenarios rather than hypothetical ones. A runbook written from an imagined failure tends to describe a failure that never happens; one written from what actually broke — and what the person on call needed to know at 2am — is the one that gets used.
Talk to an architect about your reliability.
Tell us what broke last, and how long it took to find out why — that's usually where we start.