Opsphere

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Opsphere vs Resolve AI

Both platforms aim to reduce the operational context gap across modern production systems, but they approach the problem from different product and control models.

Operational intelligence layer vs agent-driven production operations.

AT A GLANCE

Two ways to close the operational context gap

Opsphere

Primary positioning

Operational Intelligence Layer

Focus

  • Interrogating existing systems
  • Evidence-backed operational investigations
  • Reusable Operational Context
  • MCP access
  • Read-only by default

Resolve AI

Public positioning

AI for production / agents that run your software

Publicly emphasizes

  • On-call agents
  • Incident agents
  • Background operational agents
  • Model orchestration
  • Queryable production context
  • Governed actions
  • Custom agents via MCP / API / Skills

COMPARISON

Opsphere and Resolve AI, dimension by dimension

  • Primary positioning

    OpsphereOperational Intelligence Layer
    Resolve AIAI for production / operational agents

  • Core starting point

    OpsphereQuery and correlate existing operational systems
    Resolve AIAgent platform for on-call, incidents and operational tasks

  • Evidence-backed investigations

    OpsphereYes
    Resolve AIYes, publicly documented

  • Parallel investigation

    OpsphereStructured hypotheses across multiple sources
    Resolve AIAgent teams publicly described as investigating in parallel

  • Operational context

    OpsphereYes
    Resolve AIYes

  • Knowledge graph

    OpsphereOperational Knowledge Graph
    Resolve AIPublicly describes a queryable graph of services, dependencies, deploys and team knowledge

  • Investigation memory

    OpsphereStructured Investigation Memory
    Resolve AIPublicly describes learning and context retrieval

  • MCP

    OpsphereYes
    Resolve AIYes — MCP / API / Skills publicly documented

  • Atomic operational tools

    OpsphereCore Opsphere capability
    Resolve AICapabilities exposed to agents, as publicly documented

  • Read-only default

    OpsphereYes
    Resolve AIGoverned actions can perform writes within guardrails and approval

  • Production actions

    OpsphereRecommendations and governed handoff; read-only default
    Resolve AIPublicly documents actions such as silencing alerts, reverting commits, opening PRs and running workflows

  • Incident / on-call ownership

    OpsphereNot the primary product category
    Resolve AICore public product area

  • Telemetry ownership

    OpsphereSource systems remain authoritative
    Resolve AIIntegrates existing telemetry systems

  • AI / model relationship

    OpsphereModel-agnostic operational layer
    Resolve AIPublicly describes model routing and orchestration

  • Interfaces

    OpsphereWeb Client plus MCP clients including Cursor, Codex and Claude where supported
    Resolve AIResolve agents plus custom agent interfaces via MCP / API / Skills

Competitor cells describe publicly documented behavior and are re-verified against Resolve AI's official product documentation before each review.

ARCHITECTURE

Operational intelligence vs agent-driven operations

Opsphere and Resolve AI overlap strongly in production investigation and context. The main distinction is the default operating model.

Opsphere is designed as a read-only Operational Intelligence Layer. It queries and correlates systems that remain authoritative for their data, returns structured investigations, and makes that intelligence available to engineers and AI clients.

Resolve AI publicly positions agents as active participants in on-call, incidents and operational work, with governed actions capable of performing production changes under configured controls.

Opsphere prioritizes understanding and evidence before execution. Resolve AI publicly emphasizes agents that can also act.

WHERE THEY OVERLAP

Context, knowledge and MCP

Both products publicly emphasize persistent production context rather than treating each interaction as isolated.

Opsphere uses

  • Operational Knowledge Graph
  • Investigation Memory
  • Similar Incident Matching
  • Scoped Operational Context

Resolve AI publicly describes

  • A queryable graph
  • Services
  • Dependencies
  • Deploys
  • Team knowledge
  • Learning from interactions

The differentiation is less about whether context exists, and more about how that context is exposed and how far the platform proceeds from diagnosis into action.

Both support MCP — but MCP is not the whole product

Opsphere exposes through MCP

  • Atomic operational tools
  • High-level workflows
  • Structured investigations
  • Operational intelligence to external clients

Resolve AI publicly exposes through

  • MCP
  • API
  • Skills

For both platforms, MCP is an access surface. The evaluation should focus on what operational layer exists behind it.

WHERE OPSPHERE MAY FIT

Opsphere may be a strong fit for teams that prioritize

  • Read-only operational access
  • Source systems remaining authoritative
  • Broad direct tool interrogation
  • Structured evidence and verification
  • Multi-client MCP usage
  • Reusable Operational Context without starting from an autonomous-action model

WHERE RESOLVE AI MAY FIT

Resolve AI may be a strong fit for teams specifically looking for

  • AI participation in on-call
  • Incident agents
  • Background operational agents
  • Governed production actions
  • Agent-led operational execution

METHODOLOGY

How this comparison was prepared

This comparison is based on publicly available product information and Opsphere's documented current capabilities. Product features and positioning change over time. We aim to describe each platform fairly and update comparisons when material changes are identified.

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