Evidence-backed operational investigations
Opsphere investigates operational issues by evaluating multiple possible causes against evidence from the systems you already use — instead of jumping directly to a single black-box root-cause explanation. Hypotheses. Evidence. Confidence. Timeline. Verification.
OPERATIONAL INVESTIGATIONS
Root-cause analysis should show its work
Modern production issues rarely live inside one tool. A failed deployment may appear in CI/CD, application errors may surface in observability, infrastructure health may live in Kubernetes or cloud platforms, and edge behavior may sit in DNS, TLS or CDN systems.
Opsphere brings those signals together into a structured investigation so teams can see not only the conclusion, but also the evidence that supports it, the evidence that contradicts it, and what still remains uncertain.
Avoid premature root-cause conclusions
A hypothesis stays pending until the evidence is strong enough to support or reject it.
Keep facts separate from inference
What the connected systems returned is recorded distinctly from what Opsphere concluded from it.
Correlate evidence across operational domains
Deployments, errors, infrastructure state, code changes and network checks are evaluated against the same question.
Make uncertainty visible instead of hiding it
When evidence is weak or contradictory, the investigation can stay unresolved rather than forcing a confident answer.
HOW IT WORKS
A structured investigation, every time
Opsphere runs operational investigations through the same repeatable stages, using read-only access to the tools you already run.
Structured, not a single guess
Several plausible causes stay active at once and are tested against evidence instead of collapsing to one story too early.
Evidence from the systems you run
Findings are grounded in real results from infrastructure, observability, deployment, code, network and edge tools connected to Opsphere.
Output you can audit
Supporting evidence, contradicting evidence, calculated confidence and open questions are all shown — not just a conclusion.
From symptom to evidence-backed conclusion
An Opsphere investigation moves through five stages. The visual is a sequence, not a technical architecture.
- Hypotheses
- Evidence
- Confidence
- Timeline
- Verification
CAPABILITIES
Five parts of every investigation
Parallel hypotheses
Opsphere keeps several plausible causes active at the same time — deployment changes, configuration issues, infrastructure health, application behavior, external dependencies, networking or CDN conditions. A hypothesis can stay pending, become supported, be rejected, or stay unresolved when the evidence is insufficient.
Real operational evidence
Evidence comes from the operational tools connected to Opsphere — infrastructure, observability, deployment systems, code repositories, network checks and edge platforms. Evidence can support, contradict or remain neutral toward a hypothesis. Opsphere does not need to invent certainty when the underlying systems do not provide enough evidence.
Calculated confidence
Confidence comes from evidence, not model self-assessment. Opsphere derives it from the available evidence — its quality, independent supporting sources and contradictions — and reports it as HIGH, MEDIUM or LOW so teams can see how strongly the evidence supports a conclusion.
Investigation timeline
When source systems provide reliable timestamps, Opsphere organizes relevant events into a compact timeline — a deployment, an error increase, later recovery signals. The timeline is built from evidence already collected; if timestamps are unavailable, Opsphere does not fabricate a sequence.
Verification conditions
Supported conclusions and recommended next steps can include explicit verification conditions — error rate returns to baseline, Kubernetes workloads become Ready, an endpoint returns healthy HTTP responses, certificate validation succeeds, synthetic checks recover. This turns "try this" into a recommendation with a clear way to validate the outcome.
Operational reasoning without hiding uncertainty
Opsphere keeps observed facts, inference and confidence distinct. If evidence is weak or contradictory, the investigation can remain unresolved rather than forcing a confident answer — which makes the output easier to audit and safer to use in production.
Observed facts
What the connected systems actually returned.
Inference
What can reasonably be concluded from those facts.
Confidence
How strongly the available evidence supports that conclusion.
Unknowns
What still needs to be checked before closing the investigation.
One investigation across your operational stack
Operational issues cross tool boundaries. Opsphere can combine evidence from cloud infrastructure, Kubernetes, observability, deployments and CI/CD, code and repositories, DNS / HTTP / TLS, CDN and edge platforms, and security and operational controls — without teams manually reconstructing the story across separate dashboards.
Opsphere queries and correlates the systems that already own the operational data.
Structured investigations wherever engineers work
Opsphere investigations are not limited to a single interface. The Web Client can present hypotheses, evidence, confidence, timelines and verification directly; high-level capabilities exposed through MCP can also return structured investigation results to environments such as Cursor, Codex and other compatible clients.
Atomic operational tools
Query one system or capability. Atomic tools remain atomic — a single MCP tool does not perform a full RCA.
High-level investigations
Combine multiple signals into a structured investigation result with hypotheses, evidence, confidence, timeline and verification.
Investigate production without silently changing it
Opsphere is read-only by default. It can inspect, correlate, diagnose and recommend without silently mutating production systems. Verification and recommended actions can be structured, but execution stays governed by the user, team or external workflow.
Read-only by default · Tenant isolated · Environment scoped · Source systems remain authoritative · Evidence before inference
Example: a production checkout issue
A production checkout service starts returning errors shortly after a deployment. Opsphere may investigate whether a recent deployment changed, whether application errors increased, whether Kubernetes workloads remain healthy, whether network and CDN signals are normal, and whether an external dependency is degraded.
Probable cause — HIGH confidence
A recent configuration change correlates with the error increase on checkout-api.
Supporting evidence
Deployment at 14:31; error spike at 14:33; application errors tied to the same dependency; Kubernetes capacity healthy; CDN checks normal.
Verification
Error rate returns to baseline; the checkout synthetic passes; dependency latency normalizes.
KEEP EXPLORING
Related platform capabilities
GET STARTED
Investigate operational issues with evidence you can inspect
See how Opsphere structures operational investigations across your existing stack — hypotheses, evidence, confidence, timeline and verification.