Agentic Index
Sherlocks.ai vs Wild Moose (2026)
Both vendors last verified
Sherlocks.ai and Wild Moose both investigate incidents read only across observability signals. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Sherlocks correlates signals for incident response, root cause and outage prevention, with institutional memory, on a free plan of 30 investigations a month and a custom Enterprise plan. Wild Moose is an AI first responder that surfaces root cause in under a minute and runs checks the customer attaches to alerts, sold through sales with an on premises option. Choose Sherlocks to start free with memory across incidents, and Wild Moose for fast first response with an audit trail and your own checks.
On the Agentic Index production ops ranking, neither Sherlocks.ai nor Wild Moose clears the bar, which asks for all five resolution loop capabilities documented in full. Sherlocks.ai documents two of the five in full; Wild Moose does not document integrations and tool calling 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. Sherlocks.ai and Wild Moose 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 Sherlocks.ai if
- You want to start free, with 30 investigations a month.
- Institutional memory should carry across incidents.
- Correlating signals to prevent outages, not only explain them, is the goal.
Choose Wild Moose if
- Root cause should surface within a minute of the page.
- Your team's own checks should run on each alert, and results should be evaluated.
- Your own checks should run when an alert fires.
| Feature | S Sherlocks.ai |
W Wild Moose |
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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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Sherlocks.aiIntegrations & Tool Calling Sherlocks connects to 50+ tools (Datadog, New Relic, Prometheus, AWS, GCP, Azure, Kubernetes, databases) and queries logs, metrics and traces. The Watson data agent runs inside the customer's own VPC with strictly read only permissions, so agents read outside systems and report to Slack or Teams instead of acting in them. Named sources also include Sentry, Coralogix, Elasticsearch, Loki, PostgreSQL, MySQL, MongoDB, Redis, Kafka, RabbitMQ, GitHub Actions and Jenkins. SourceSherlocks.ai, sherlocks.airead 2026-10-05 |
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Wild MooseIntegrations & Tool Calling Wild Moose connects observability tools, code repos, CI/CD pipelines and internal tools, but all integrations run strictly read only. Its agents read outside systems and report to Slack or Teams, and do not act in them. Named integrations span alerting (PagerDuty, Incident.io, Squadcast, OpsGenie, FireHydrant), observability and data (Grafana, Datadog, New Relic, Sumo Logic, Coralogix, Prometheus, Loki, Tempo, Zabbix, Snowflake, ClickHouse, Trino) and knowledge bases (Jira, Fibery, Zendesk, Confluence, Notion). SourceWild Moose, wildmoose.ai/security and /integrationsread 2026-10-05 |
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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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Sherlocks.aiWorkflow Orchestration Each investigation runs 16+ domain specialized agents in parallel. Sherlocks maps the alert to its knowledge graph, forms a hypothesis, queries logs, metrics and traces to validate it, then reports root cause with short and long term remediations. Findings arrive in Slack or Microsoft Teams. SourceSherlocks.ai, sherlocks.airead 2026-10-05 |
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Wild MooseWorkflow Orchestration Micro-agents specific to each company work as a coordinated system, running investigations in parallel, and are built and managed as code. A customer describes a check, tests it and iterates on it. Triage agents start each investigation, and specialized agents deploy based on the signals they find. SourceWild Moose, wildmoose.ai/productread 2026-10-05 |
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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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Sherlocks.aiTriggers & Channel Coverage The alert itself triggers Sherlocks, which investigates on its own with 16+ domain specialized agents and reports back in Slack, and tickets and Slack requests also start investigations. The free plan covers 30 investigations a month, and Enterprise is unlimited. SourceSherlocks.ai, sherlocks.ai and /pricingread 2026-10-05 |
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Wild MooseTriggers & Channel Coverage When an alert fires, Wild Moose activates triage agents to run checks, pull evidence and narrow down the most likely root cause. Alerts come from PagerDuty, Incident.io, Squadcast, OpsGenie and FireHydrant, and alert enrichment takes under a minute. SourceWild Moose, wildmoose.ai/product, /integrations and wildmoose.airead 2026-10-05 |
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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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Sherlocks.aiKnowledge Grounding & RAG Sherlocks maps each alert to a knowledge graph of the customer's infrastructure and applications, built automatically, that persists across investigations and grounds each hypothesis before it queries telemetry. SourceSherlocks.ai, sherlocks.airead 2026-10-05 |
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Wild MooseKnowledge Grounding & RAG Each investigation gathers logs, metrics, traces and code from the connected tools at alert time, processed in memory and discarded afterward. Wild Moose says it learns the customer's infrastructure and ingests historical incident data to understand the environment, and it connects knowledge bases such as Jira, Confluence, Notion, Zendesk and Fibery. It names no index, graph or knowledge store it maintains. SourceWild Moose, wildmoose.ai, /product and /integrationsread 2026-10-05 |
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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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Sherlocks.aiMemory & State Persistence Sherlocks carries institutional memory across infrastructure, past incidents, runbooks and Slack conversations, so each investigation starts with the context the team has already built up. That memory carries across runs, but Sherlocks does not say what it covers, how long it lasts or how to delete it. SourceSherlocks.ai, sherlocks.airead 2026-10-05 |
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Wild MooseMemory & State Persistence Gaps an investigation finds become new or updated micro-agents committed as a pull request. These are procedures the product runs, and Wild Moose names no memory the agents read. Processing is ephemeral with no data retention, and when Wild Moose says it learns from each incident, that learning is this update loop. Wild Moose stores no logs, metrics or other raw data. SourceWild Moose, wildmoose.ai/product and /securityread 2026-10-05 |
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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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Sherlocks.aiHuman Oversight & Guardrails The data agent has strictly read only permissions, and Sherlocks reports remediations for the team to carry out, so action stays with people. Sherlocks describes no step that presents a fix for approval. Remediations come in short term and long term versions. SourceSherlocks.ai, sherlocks.airead 2026-10-05 |
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Wild MooseHuman Oversight & Guardrails Integrations are strictly read only, and the agents suggest fixes for engineers to carry out, so action stays with people. Agent changes arrive as pull requests validated against past incidents and gated by the customer's code host. Wild Moose names no approval step inside the product. SourceWild Moose, wildmoose.ai/product and /securityread 2026-10-05 |
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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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Sherlocks.aiSecurity, Identity & Governance The Enterprise plan adds SSO and SAML, audit logs and RBAC, with a read only in VPC data agent, and compliance rests on SOC 2 Type 2, with security review support. Data is encrypted in transit and at rest, and the free plan has no SSO, audit logs or RBAC. SourceSherlocks.ai, sherlocks.ai/pricing and sherlocks.airead 2026-10-05 |
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Wild MooseSecurity, Identity & Governance The company says it is SOC 2 Type II certified and adheres to SOC 2 standards for security, availability and confidentiality, with end to end encryption, no data retention and no training on customer data. Wild Moose names no SSO, roles or permissions model. SourceWild Moose, wildmoose.ai/securityread 2026-10-05 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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Sherlocks.aiObservability & Auditability Each investigation reports a root cause with remediations in Slack, and Enterprise includes audit logs, but Sherlocks names no record per investigation of the queries and steps the agents took. Reports also go to Microsoft Teams. SourceSherlocks.ai, sherlocks.ai and /pricingread 2026-10-05 |
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Wild MooseObservability & Auditability Every investigation step is visible, from the data sources queried to findings, AI reasoning and recommendations, beside a structured root cause analysis with key signals, ranked hypotheses and next steps. Each run keeps a record of what the agents queried. SourceWild Moose, wildmoose.ai/productread 2026-10-05 |
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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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Sherlocks.aiDeployment & Data Residency The Watson data agent runs inside the customer's own VPC, so raw telemetry never leaves the customer's environment, and Enterprise adds an air gapped deployment option and an option to run the LLM in the VPC. The VPC data agent comes with the free plan too. SourceSherlocks.ai, sherlocks.ai/pricing and sherlocks.airead 2026-10-05 |
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Wild MooseDeployment & Data Residency With on prem support the customer can deploy Wild Moose within its own infrastructure and control where data is processed, and hosted processing runs in memory and is ephemeral, with no data retained outside the customer's network. SourceWild Moose, wildmoose.ai/securityread 2026-10-05 |
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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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Sherlocks.aiPrebuilt Agents, Templates & Packs The 16+ domain specialized agents run together inside each investigation, not as agents a customer selects or adopts separately. None of them is named, and the earlier Database Sherlock and Kubernetes Sherlock names no longer appear. Every plan includes all 16+ agents, and Enterprise adds a dedicated field engineer for setup. SourceSherlocks.ai, sherlocks.ai and /pricingread 2026-10-05 |
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Wild MoosePrebuilt Agents, Templates & Packs Micro-agents are checks specific to each company, built and managed as code for each customer, and the customer assembles them. Wild Moose publishes no prebuilt agents or templates. When an alert fires, triage agents run first and specialized agents follow based on what they find. SourceWild Moose, wildmoose.ai/productread 2026-10-05 |
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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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Sherlocks.aiModel Flexibility & Routing Enterprise offers an 'In-VPC LLM option', so the model can run in the customer's own environment. Sherlocks names no model or maker, and no way to choose between makers. Sourcesherlocks.ai/pricingread 2026-09-29 |
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Wild MooseModel Flexibility & Routing Wild Moose names neither a model provider nor any customer model choice. It does not fine tune or train AI models on customer data. SourceWild Moose, wildmoose.ai/securityread 2026-10-05 |
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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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Sherlocks.aiAPIs, SDKs & MCP Extensibility No API, SDK or MCP server is published for driving Sherlocks from outside. Teams work with Sherlocks through Slack and Microsoft Teams. SourceSherlocks.ai, sherlocks.airead 2026-10-05 |
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Wild MooseAPIs, SDKs & MCP Extensibility There is no published API, SDK or MCP server for driving Wild Moose, and there are no docs or developer pages. Customers manage micro-agents as code, but these are their own checks and do not drive the platform. Results reach teams in Slack or Microsoft Teams. SourceWild Moose, wildmoose.ai/product and /integrationsread 2026-10-05 |
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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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Sherlocks.aiTesting, Debugging & Optimization No evaluation harness, scored tests or improvement loop is named for the agents' investigations. Sherlocks reports MTTR falling from hours to minutes, alert noise down 90% and downtime down 70%. SourceSherlocks.ai, sherlocks.airead 2026-10-05 |
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Wild MooseTesting, Debugging & Optimization Micro-agents are tested against real past incidents. A customer describes a check, tests it against historical incidents and iterates until it performs reliably, and each new or updated agent is validated against past incidents before it runs in production. Wild Moose reports 90% accuracy within three weeks and, at Wix, root cause accuracy above 80%. SourceWild Moose, wildmoose.ai/product and wildmoose.airead 2026-10-05 |
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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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Sherlocks.aiBrowser & Computer Use Agents query telemetry through integrations and report in Slack and Teams. Sherlocks names no browser, desktop or computer control. Sourcesherlocks.airead 2026-09-29 |
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Wild MooseBrowser & Computer Use Agents read connected tools through read only integrations. Wild Moose names no browser, desktop or computer control. Alerts arrive from tools such as PagerDuty and OpsGenie, and answers go to Slack or Teams. SourceWild Moose, wildmoose.ai/product and /integrationsread 2026-10-05 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | S Sherlocks.ai |
W Wild Moose |
|---|---|---|
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Entry price Lowest public entry point |
Free (30 investigations a month, self serve); Enterprise custom | Contact sales; enterprise with on-premise option |
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Pricing confidence How public the numbers are |
Public, partial | Contact only |
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Billing Primary billing axis |
Investigations per month on Free; custom Enterprise contract | — |
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Variable cost Workload / overage exposure |
Medium variable cost | High variable cost |
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Free tier / trial Try before you buy |
Free tier
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No free tier
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Buying motion Self-serve vs sales call |
Mixed | Sales call |
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Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Sherlocks.ai
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· Integrations
Sherlocks.ai introduced an AI SRE integration for Slack that makes the chat platform a primary incident investigation interface.
Source
Sherlocks has a free plan and a custom Enterprise plan, and Wild Moose sells through sales.
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