Frontier Investigationfor Performance and Latency
Slow endpoints traced to the query, cache or code path behind them.
Slow is harder to fix than down. Agents take a slow endpoint, follow the traces into the code, the queries, the cache and the connection pool, and hand your team the exact path that got slower and why. Each finding comes with a fix and the latency it should win back.
GET /search p95 up from 380ms to 1.9s
- Traces
- 78% of time in ProductRepository.findWithFilters
- Change
- Release 5.3.0 added a facet filter on 2 joined tables
- Cause
- N+1 queries: 1 query per facet, up to 64 per request
- Evidence
- 20 slow traces, query log, flame graph, the diff
- Fix
- Batch the facet query. Staging p95 at 410ms
Investigated in 6m. Waiting for team review
[The work behind every slow endpoint]
Customers feel latency first.Your team finds it last.
01The manual work
Profile
A slow dashboard is only the start. Engineers still sample traces, read flame graphs and guess which release made it worse.
02The agent handoff
Hot path found
Frontier agents follow the slow traces into code, queries and infrastructure, and prepare a fix with the expected gain.
03Your engineers’ role
Approve
Set your latency targets. Review the proposed fix and decide when it ships.
[Where CloudThinker fits]
Your stack stays.Agents work inside it.
01Signal sources
Already in your APM
Datadog APMTraces, spans and latency monitors
Amazon CloudWatchApplication Signals and X-Ray
Application InsightsRequests, dependencies and traces
Google Cloud TraceLatency distributions per endpoint
New Relic and DynatraceTransaction traces and baselines
No change to your agents
02Code and data
Your system of record
GitHub and GitLabCommits, diffs and releases
PostgreSQL and MySQLQuery plans and slow logs
RedisCache hit rates and evictions- SentryErrors tied to slow requests
Source of truth stays put
03Investigation
CloudThinker
- Find the slow pathThe span that owns the latency
- Trace the causeQuery, cache, pool or code change
- ProvePlans, traces and diffs linked
- ProposeA fix with a before and after test
Read-only by default
04Response
Faster, with proof
- Pull requestIndex, cache or code fix to review
SlackFindings posted to the team channel- JiraTicket with the trace attached
GrafanaLatency verified after the fix
Fix ships on approval
[Example scenario]
Black Friday minus 3 days.Search just got five times slower.
A 35-engineer retail team on ECS and Aurora PostgreSQL, with Datadog APM. Search drives 40% of orders, and the sale starts Friday at 00:00.
- 09:12
Latency monitor firesSignal
Datadog: GET /search p95 at 1.9s against a 500ms target. No errors, so nobody was paged overnight.
- 09:13
Agent follows the tracesAgent
Samples 20 slow traces. 78% of the time sits in one repository method that now runs dozens of queries per request.
- 09:16
Tied to a releaseAgent
Release 5.3.0 on Monday added a facet filter. Each facet triggers its own query: an N+1 pattern, up to 64 per search.
- 09:19
Fix tested in stagingAgent
Pull request batches the facet query into one call. Load test in staging shows p95 back at 410ms.
- 10:30
Team lead approvesYour team
Reviews the flame graph and the diff, approves the pull request for the 11:00 deploy.
- 11:20
Verified in productionAgent
p95 at 395ms for 20 minutes. Agent adds a latency check for this endpoint to the release pipeline.
Datadog09:12
Warn: GET /search p95 latency 1.9s (target 500ms) for 30 min.
CloudThinker09:19
Cause: N+1 in ProductRepository.findWithFilters since release 5.3.0, up to 64 queries per search. PR #4471 batches it into 1 query. Staging p95: 410ms.
Search team lead10:30
Makes sense, the facet change was mine. Approved for the 11:00 deploy.
CloudThinker11:20
Deployed. p95 at 395ms and steady. Added a p95 budget check for /search to the release pipeline.
- from monitor to tested fix
- 7 min
- search p95, before the sale
- 1.9s to 395ms
- flame graphs read by hand
- 0
An illustrative example. Team, systems and times are representative, not a specific customer.
[Frontier investigation agents]
Every slow path gets traced.Your team gets the fix.
Agents follow latency from the request down to the line of code or the query, so your team ships a measured fix, and slow paths get caught before customers feel them.
- Follow the traces
- Slow requests sampled and broken down by service, method, query and external call.
- Find the slow query
- N+1 patterns, missing indexes, lock waits and exhausted connection pools traced to their source.
- Check cache and capacity
- Cache hit rates, CDN behaviour, memory leaks and CPU saturation lined up against the slowdown.
- Ship a measured fix
- Each fix comes with a staging measurement, then gets verified against your latency target in production.
[What changes]
Same team. Same tools.Far less of the work by hand.
| Moment | Today | With frontier agents |
|---|---|---|
| Noticing a slowdown | When customers complain | When the latency target slips |
| Finding the cause | Hours in traces and flame graphs | The hot path, the release and the query, linked |
| Proposing a fix | A guess, tried in production | A pull request measured in staging |
| After the fix | Hope it holds | Verified against the target, with a pipeline check |
| Who does it | The one engineer who knows profiling | Any engineer, with the evidence in front of them |
[Integrations]
Connects to the rest of your stack.Read-only to start.
- Datadog
- New Relic
- Dynatrace
- Grafana
- Prometheus
- Honeycomb
- Sentry
- Elasticsearch
- Amazon CloudWatch
- Amazon RDS
- GitHub
- GitLab
[Adoption path]
One pilot.Then company-wide.
The rollout follows the four phases of the AWS Cloud Adoption Framework, so it fits the plan your cloud team already runs.
01Envision
Pick your slowest endpoints
Connect APM read-only and let agents investigate your top slow endpoints. Compare their causes with what your team knows.
02Align
Set latency targets
Agree the p95 or p99 target per service, and which fixes need which approval.
03Launch
Roll out by service
Add each team’s services on the same targets, so every slowdown gets the same investigation.
04Scale
Guard every release
Latency checks join the release pipeline. Regressions are caught before customers feel them.
[AWS guidance]
Built the way AWS says to run it.Reviews start from a known reference.
[Trust and control]
Agents do the work.Your team keeps control.
- You approve every change
- Agents propose. Nothing touches production until someone on your team says yes, and you set that rule per system.
- Every action on the record
- Each step is logged, attributed and reversible, ready for your auditors.
- Certified for enterprise
- SOC 2 Type II and ISO 42001, with reports in our trust center.
- Runs where you need it
- In our cloud, through AWS Marketplace, or inside your own account.
[Questions]
What teams askbefore they start.
- Do we need a specific APM tool?
- No. Agents read from the tracing and metrics you already run, such as Datadog, New Relic, Dynatrace, Grafana or CloudWatch.
- Can it find slow database queries?
- Yes. Slow traces are followed into the queries they run, including N+1 patterns, missing indexes and lock waits.
- Does it change production on its own?
- Only where your policy allows it. Most teams review each fix as a pull request, measured in staging first.
- What if there is no clear cause?
- The report says so, shows what was ruled out and suggests the instrumentation that would close the gap.
[Go deeper]
Related reading.Pick the next queue.
Trace every slow path.Before your customers notice it.
Start with your slowest endpoints, read-only. See the causes agents find before you approve a single change.
Up to $200K in AWS credits
Applied to your own AWS account.
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