Agentic Index
Cleric vs Wild Moose (2026)
Both vendors last verified
Cleric and Wild Moose both investigate production incidents with AI, and they part ways after the first investigation. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Cleric verifies each change after merge against live production, investigates alerts read only and hands fix pull requests to engineers, with memories between investigations and credit plans whose Team tier is $700 a month. Wild Moose is an AI first responder that investigates across observability signals and surfaces root cause in under a minute, working read only and running checks the customer attaches to alerts, sold through sales with an on premises option. Cleric also takes calls from outside agents through its own MCP server on every plan and scopes access by integration. Choose Cleric for verification after merge, memory and an MCP server at a public price. Choose Wild Moose for fast first response driven by your own checks.
On the Agentic Index production ops ranking, neither Cleric nor Wild Moose clears the bar, which asks for all five resolution loop capabilities documented in full. Cleric does not document workflow orchestration in full, nor human oversight and guardrails; 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. Cleric 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 Cleric if
- Every change should be verified against live production after it merges.
- Context should carry from one investigation to the next.
- You want a public price and an MCP server for your own agents.
Choose Wild Moose if
- Root cause should surface within a minute of the page.
- Your team's own checks should run automatically on each alert.
- You need an on premises option.
| Feature | C Cleric |
W Wild Moose |
|---|---|---|
| Action & orchestration | ||
|
Integrations & Tool Calling Ability to connect agents to real systems through native integrations, OAuth-authenticated actions, custom tools, APIs, webhooks, or MCP-compatible tools. |
||
|
ClericIntegrations & Tool Calling Cleric has named integrations across observability, infrastructure, code and documentation (AWS, Azure, Datadog, Grafana, PagerDuty, Slack, Atlassian, GitHub), with scoped access for each. The agent runs its own commands and queries against them and opens pull requests with fixes. Sourcecleric.airead 2026-09-29 |
||
|
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 |
||
|
Workflow Orchestration Ability to sequence, branch, retry, route, and combine deterministic workflow nodes with autonomous agent steps. |
||
|
ClericWorkflow Orchestration One investigating agent plans tasks, runs queries and adjusts its plan. Custom agents are a name plus a free text prompt fired by a trigger, with no steps, branching or handoffs between agents, and the built in investigate agent cannot be edited. Sourcedocs.cleric.ai/setup/agents-and-triggersread 2026-09-29 |
||
|
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 |
||
|
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. |
||
|
ClericTriggers & Channel Coverage Agents fire on bot messages posted in Slack channels that match trigger keywords (alert bots such as PagerDuty and Datadog) and on recurring cron schedules, and change verification follows each change after merge. Scheduled agents and monitors are on the Pro plan. Sourcedocs.cleric.ai/setup/agents-and-triggersread 2026-09-29 |
||
|
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 |
||
| Knowledge & context | ||
|
Knowledge Grounding & RAG Ability to ground agent behavior in company data through document ingestion, retrieval, external knowledge APIs, semantic search, or RAG layers. |
||
|
ClericKnowledge Grounding & RAG Documentation integrations (Atlassian, GitHub) let the agent search and retrieve documentation during investigations, and customers add Global Guidance in settings. Context is assembled for each run, and there is no maintained index over the customer's documents. Sourcedocs.cleric.ai/setup/providing-contextread 2026-09-29 |
||
|
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 |
||
|
Memory & State Persistence Ability to persist context across a run, conversation, workflow, user, team, or longer-term memory layer. |
||
|
ClericMemory & State Persistence Memories are facts Cleric proposes from conversations or users add with '@Cleric Remember', organized by service and environment, searched automatically during investigations, kept until deleted or removed by automatic cleanup when stale or contradicted, and viewable, editable and deletable in the Knowledge section. Sourcedocs.cleric.ai/learning/memoriesread 2026-09-29 |
||
|
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 |
||
| Control & trust | ||
|
Human Oversight & Guardrails Approval steps, consent checkpoints, escalation rules, structured guardrails, policy constraints, and pause/resume controls. |
||
|
ClericHuman Oversight & Guardrails Access is read only, enforced by RBAC and access scopes. Cleric does not make changes to customer infrastructure but may propose them, and all remediation actions are performed by the customer's team. People keep the decisions, and the agent's reach is scoped. Fix pull requests are approved in the customer's own code host. The approve button in the homepage mockup is not explained as a Cleric approval step. Sourcedocs.cleric.ai/security/security-data-privacyread 2026-09-29 |
||
|
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 |
||
|
Security, Identity & Governance RBAC, SSO, auditability, encryption, least-privilege tool access, compliance posture, and data handling policy. |
||
|
ClericSecurity, Identity & Governance Customers control access through RBAC and access scopes for each integration, and code access can be limited to one repository. Every plan has Google Workspace sign in, and Pro adds Okta, custom SAML or OIDC and an organization audit log. Cleric holds SOC 2 Type II, has a Trust Center and runs annual penetration testing, with AES-256 encryption at rest. Sourcedocs.cleric.ai/security/security-data-privacyread 2026-09-29 |
||
|
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 |
||
|
Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
||
|
ClericObservability & Auditability Each investigation keeps an activity log recording the plan, the tools used, the specific commands and queries executed, the raw output from each tool and the reasoning behind conclusions, viewable in the web app. Retained investigation data includes queries, reasoning traces and cited evidence. Sourcedocs.cleric.ai/usage/reviewing-the-activity-logread 2026-09-29 |
||
|
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 |
||
|
Deployment & Data Residency Deployment modes and options, including SaaS, dedicated cloud, VPC, on-prem, hybrid, local runtime, and self-hosting. |
||
|
ClericDeployment & Data Residency EU hosted instances run the application, database and model inference in Europe, beside the default GCP hosting, and Enterprise offers custom data retention and region. Operational logs, traces and product analytics are still processed centrally in the US. A private network connector reaches private resources but does not change where Cleric runs. Sourcedocs.cleric.ai/security/eu-hosting-data-processingread 2026-09-29 |
||
|
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 |
||
| Solution readiness | ||
|
Prebuilt Agents, Templates & Packs Ready-made workflows, packaged employees, templates, blueprints, industry solutions, and role-specific agents that reduce time-to-value. |
||
|
ClericPrebuilt Agents, Templates & Packs One built in investigate agent ships and cannot be edited. Further agents are ones the customer writes as a name and a prompt, and there is no template or agent catalog. Sourcedocs.cleric.ai/setup/agents-and-triggersread 2026-09-29 |
||
|
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 |
||
| Platform extensibility | ||
|
Model Flexibility & Routing Ability to work across multiple foundation models, route tasks to different models, or let buyers bring their own providers and keys. |
||
|
ClericModel Flexibility & Routing Cleric names Anthropic, Google Gemini and OpenAI as its model providers, under zero training and zero retention terms. Cleric uses them internally, and customers and admins cannot choose a model. Sourcedocs.cleric.ai/security/security-data-privacyread 2026-09-29 |
||
|
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 |
||
|
APIs, SDKs & MCP Extensibility Composability layer: stable APIs, SDKs, MCP tool consumption/serving, custom tools, and integration into internal systems. |
||
|
ClericAPIs, SDKs & MCP Extensibility Outside callers drive Cleric through its own MCP server, on every plan. It has a per tenant endpoint at <company>.app.cleric.ai/mcp, bearer authentication with personal API keys and an enumerated tool list, and its create_issue tool starts a new investigation, a core platform object. There is no REST API or SDK. Sourcedocs.cleric.ai/integrations/mcpread 2026-09-29 |
||
|
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 |
||
|
Testing, Debugging & Optimization Testing, debugging, scoring, retries, fallbacks, quality gates, and optimization loops for improving agent workflows before and after deployment. |
||
|
ClericTesting, Debugging & Optimization A learning loop calibrates the agent through explicit feedback (1 to 5 message ratings and corrections), implicit feedback from actions taken, and analysis of past investigations. There is no evaluation harness, scored test or quality metric for findings. Change verification tests the customer's releases, not the agent. Sourcedocs.cleric.ai/learning/how-cleric-learnsread 2026-09-29 |
||
|
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 |
||
| Specialist automation | ||
|
Browser & Computer Use Browser, desktop, or remote/local computer control for workflows that cannot be handled through stable APIs alone. |
||
|
ClericBrowser & Computer Use Work runs through scoped integrations, commands and queries, Slack and an MCP server, with no browser, desktop or computer control. Sourcedocs.cleric.ai/usage/reviewing-the-activity-logread 2026-09-29 |
||
|
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 |
||
Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | C Cleric |
W Wild Moose |
|---|---|---|
|
Entry price Lowest public entry point |
From $700 per month on Team, with 1,400 credits. Pro is $2,000 and Enterprise is custom. | Contact sales; enterprise with on-premise option |
|
Pricing confidence How public the numbers are |
Public, exact | Contact only |
|
Billing Primary billing axis |
Monthly credit plans. A change verification or an investigation draws 10 credits and chat 1 credit per minute, and Enterprise runs on a custom annual credit pool. | — |
|
Variable cost Workload / overage exposure |
Medium variable cost | High variable cost |
|
Free tier / trial Try before you buy |
No free tierTrial
|
No free tier
|
|
Buying motion Self-serve vs sales call |
Mixed | Sales call |
|
Pricing verified When this pricing was last checked |
Product updates
Newest first, each linked to its source.
Cleric's Pro plan is $2,000 a month, and Wild Moose sells through sales with no public price.
More comparisons with Cleric or Wild Moose
Other matchups in SRE and DevOps agents
Not the pairing you were after? These compare a different set of SRE and DevOps agents on the same 14 capabilities.