Agentic Index
Avesha vs Resolve AI (2026)
Avesha last verified ·Resolve AI last verified
Avesha and Resolve AI are both AI SREs sold through sales with no public price, and they differ on who acts. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Avesha's Obliq watches CI and telemetry for Kubernetes and agentic workloads, works out root cause and executes rollbacks, replica bursts or configuration patches under risk scores and policy as code. Resolve goes on call with a team of specialized agents that triage and investigate in parallel, and its actions wait for a person's approval by default. Choose Avesha for autonomous remediation under policy you write, and Resolve for investigation with a person approving fixes.
On the Agentic Index production ops ranking, Resolve AI clears the bar and Avesha does not. Resolve AI documents all five resolution loop capabilities in full; Avesha does not document observability and auditability in full, nor human oversight and guardrails. 15 of the 38 vendors in the lane clear it. See the production ops ranking
This comparison is published by Agentic Index, an independent agentic AI vendor research platform. Avesha and Resolve AI are each graded against the same 14 capability Agentic Index taxonomy, from the vendor's own public materials under the Agentic Index verification standard, alongside 1,084 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Avesha if
- Agents should remediate on their own under risk scores and policy as code.
- Several deployment options matter to you.
- Workloads include agentic systems as well as Kubernetes services.
Choose Resolve AI if
- Actions should wait for a person's approval by default.
- A person approves alert silences and fix pull requests, and the record of who approved each one is kept.
- Your developers need a documented API.
| Feature | A Avesha |
R Resolve AI |
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| Action & orchestration | ||
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Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
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AveshaIntegrations & Tool Calling Obliq's Auto-Remediator executes rollbacks, replica bursts and configuration hot patches on the customer's Kubernetes, and its agents read metrics, traces, logs and CI and pull request activity. RBAC integrates with existing identity providers. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIIntegrations & Tool Calling An integration catalog of about 40 systems covers AWS, GCP, Azure, Datadog, Dynatrace, Splunk, Grafana, New Relic, PagerDuty, ServiceNow, Sentry, Snowflake, GitHub, Atlassian, Slack and Microsoft Teams, plus any connected MCP server. On approval the agent creates silences, downtimes or mutes in Grafana, Datadog, AlertManager, Kloudfuse and Sumo Logic, opens fix pull requests and dispatches GitHub Actions workflows. SourceResolve docs llms.txt index, Mitigation Actions and Security and Administration; docs.resolve.ai/mitigation-actionsread 2026-10-22 |
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Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
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AveshaWorkflow Orchestration Three agents hand work along a chain, a Code-Drift Analyzer, a Root-Cause Synthesizer and an Auto-Remediator, and declarative playbooks trigger validated rollbacks or auto-patches under custom SLAs and policies. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIWorkflow Orchestration A team of domain-specialized agents investigates in parallel, pursuing hypotheses that engineers can redirect, and verifies findings against production evidence, with a dedicated reviewer validating them. Custom agents compose Resolve's MCP, API and skills into the customer's workflows, and Temporal connects orchestration platforms. Sourceresolve.ai/product/incidentsread 2026-10-22 |
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Triggers & Channel Coverage How agents wake up and where they work: schedules, webhooks, message events, CRM events, inbox events, chat, email, voice, and collaboration tools. |
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AveshaTriggers & Channel Coverage Obliq watches CI, pull requests and GenAI commits to flag untested diffs before rollout, and acts on telemetry anomalies and policy violations in real time, detecting and self healing before humans are paged. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AITriggers & Channel Coverage The On-call Agent goes on call on the customer's behalf, holding the pager and triaging alerts. Alerts arrive by PagerDuty, Splunk On-Call and alert webhooks from any source. Tasks run on a schedule or cron, when a matching message appears in a watched Slack or Microsoft Teams channel, or on demand, and the Teams app investigates alerts posted to alert channels. Sourcedocs.resolve.ai/tasksread 2026-10-22 |
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| Knowledge & context | ||
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Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
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AveshaKnowledge Grounding & RAG Root cause work correlates live metrics, traces, logs, slice health and model telemetry into a causal narrative per incident. There is no maintained index over runbooks or knowledge bases. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIKnowledge Grounding & RAG Team Knowledge is a maintained store of runbooks, docs, dashboard guidance and skills at organization, team and personal level that Resolve activates dynamically during investigations, examining all knowledge tied to the alert and its owning teams. Skills follow the open Agent Skills format and load on demand, and Hivemind adds production state awareness across alerts and incidents. Sourcedocs.resolve.ai/team-knowledgeread 2026-10-22 |
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Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
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AveshaMemory & State Persistence Telemetry is stitched with memory of prompt, tool and outcome, and Obliq learns from prior decisions to improve outcomes, but Avesha gives that memory no stated scope, lifetime or customer control. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIMemory & State Persistence The agent keeps learnings it has accumulated, which its tasks can read, and proposes new runbooks and edits to team docs from a conversation or by reviewing completed investigations for lessons. Resolve does not say how those learnings are stored, reviewed or deleted, and Team Knowledge itself is written by people. Sourcedocs.resolve.ai/tasksread 2026-10-22 |
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| Control & trust | ||
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Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
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AveshaHuman Oversight & Guardrails Customer controlled constraints govern what the agent may do. Obliq calculates risk scores, enforces custom SLAs and policies through policy as code, and applies time bound access controls. There is no step where a person approves an action before it runs. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIHuman Oversight & Guardrails Alert silences and fix pull requests run only after a person approves them in the Resolve UI or Slack. GitHub Actions workflow tools, in preview, also default to approval, and an admin can let an individual tool run on its own. The model that proposes actions never calls write APIs, which a separate engine executes, and write permissions are enabled per integration. Sourcedocs.resolve.ai/security-and-administrationread 2026-10-22 |
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Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
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AveshaSecurity, Identity & Governance Access runs on policy as code and RBAC integration with existing identity providers, with time bound access controls. There is no published certification or trust center. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AISecurity, Identity & Governance Customers get SSO (Google, Okta, Azure AD), SCIM directory sync with sign in enforcement, RBAC, access limited to roles, environments or namespaces, and audit logs. Compliance covers SOC 2 Type II, GDPR and HIPAA, backed by a trust center. SourceResolve security page and docs llms.txt index (Security and Administration pages); resolve.ai/securityread 2026-10-22 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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AveshaObservability & Auditability Each incident gets a causal narrative, and Obliq offers audit hooks for internal compliance, but Avesha does not describe a per run record of the steps the agents took or the actions the Auto-Remediator executed. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIObservability & Auditability Each investigation shows an Evidence Trail with timestamps and verification and a Workbench of what each agent is doing. An Activity Dashboard records every action, including who proposed it, who approved or rejected it, when, and what was affected. Sourcedocs.resolve.ai/mitigation-actionsread 2026-10-22 |
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Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
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AveshaDeployment & Data Residency Obliq needs a Kubernetes cluster, Helm 3.8 or later and cluster admin access, and it deploys as a single Helm chart into the customer's own cluster, with integration pages for AWS, Google Cloud and Oracle Kubernetes Engine. SourceAvesha Obliq 1.5.0 prerequisites and repo.obliq.avesha.io; docs.avesha.io/documentation/enterprise-obliq/1.5.0/prerequisitesread 2026-09-30 |
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Resolve AIDeployment & Data Residency Customers choose between a shared multi tenant cloud and a dedicated single tenant cloud deployment with bring your own keys. The Satellite agent runs in the customer's Kubernetes or ECS, keeps credentials local and proxies queries into Resolve's cloud. There is no region list or customer environment option. Sourcedocs.resolve.ai/resolve-satelliteread 2026-10-22 |
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| Solution readiness | ||
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Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
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AveshaPrebuilt Agents, Templates & Packs Named prebuilt agents each have their own job. The Code-Drift Analyzer flags untested diffs from CI and pull requests on its own, beside the Root-Cause Synthesizer and the Auto-Remediator, with declarative playbooks. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIPrebuilt Agents, Templates & Packs Separately named agents ship for their own jobs, namely the On-call Agent that holds the pager and triages alerts, the Incident Resolution Agent for root cause and mitigation, and Issue Remediation for recurring problems. Background agents for operational tasks start from a template library that includes a deploy health monitor, a daily digest, an alert triager and a resource report. Sourceresolve.ai/product/operational-tasksread 2026-10-22 |
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| Platform extensibility | ||
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Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
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AveshaModel Flexibility & Routing The Obliq 1.5.0 integrations let the customer configure OpenAI with its own API key and choose a model from a dropdown, configure Google Gemini the same way, or point the agent at any endpoint following the OpenAI chat completions format with its own key and a custom model ID. So the customer chooses across model makers and can bring its own model. Sourcedocs.avesha.io/documentation/enterprise-obliq/1.5.0/integrations/openai-compatibleread 2026-09-30 |
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Resolve AIModel Flexibility & Routing Resolve uses frontier LLM models combined with domain-specific models, without naming a provider, and customers cannot choose a model. Sourceresolve.airead 2026-10-22 |
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APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
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AveshaAPIs, SDKs & MCP Extensibility Obliq is called MCP-Ready, but no endpoint, auth scheme or tool list is published, and there is no API or SDK for Obliq. The MCP services connect outward to the customer's AWS, Prometheus, Loki and CloudWatch and do not give outside callers a way in. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIAPIs, SDKs & MCP Extensibility A REST API at app0.resolve.ai with bearer personal or organization tokens triggers, lists and reads investigations, alongside a Resolve MCP server, plugins for Claude Code, Codex and Cursor, and skills in the open Agent Skills format. Sourcedocs.resolve.ai/resolve-rest-apiread 2026-10-22 |
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Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
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AveshaTesting, Debugging & Optimization Avesha describes a learning loop in one sentence, in which incidents feed reinforcement learning loops and the system upgrades itself. Flagging untested diffs tests the customer's code, not the agent, and there is no evaluation harness. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AITesting, Debugging & Optimization A change can be evaluated against an earlier result. After updating knowledge or integrations, Reinvestigate alert re-runs the same alert so the new result can be compared against the original, and thumbs up or down verdicts on each report collect under Analytics, Feedback. Sourcedocs.resolve.ai/tuning-accuracyread 2026-10-22 |
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| Specialist automation | ||
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Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
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AveshaBrowser & Computer Use Obliq acts on Kubernetes and cloud through APIs and playbooks. There is no browser, desktop or computer control. Sourceavesha.io/obliqread 2026-09-29 |
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Resolve AIBrowser & Computer Use Work runs through integrations, a Satellite proxy, Slack, Microsoft Teams, a REST API and an MCP server, with no browser, desktop or computer control. SourceResolve docs llms.txt index and Incidents page; resolve.ai/product/incidentsread 2026-10-22 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | A Avesha |
R Resolve AI |
|---|---|---|
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Entry price Lowest public entry point |
Enterprise pricing through sales. No price is published. | Contact sales. Contracts are metered in credits, and no rates are published. |
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Pricing confidence How public the numbers are |
Contact only | Contact only |
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Billing Primary billing axis |
Enterprise engagements scoped to environment size and workloads. The agent is self hosted, with credentials provisioned by Avesha. | Credits per unit of work, consumed by chats and tasks, investigations, incidents and triages. The credit cost of each is defined in the contract. |
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Variable cost Workload / overage exposure |
High variable cost | Medium variable cost |
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Free tier / trial Try before you buy |
No free tier
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No free tierTrial
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Buying motion Self-serve vs sales call |
Sales call | Sales call |
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Pricing verified When this pricing was last checked |
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