Agentic Index
Causely vs Cleric (2026)
Causely and Cleric both work out what broke in production, by different methods, and Cleric edges the grid, 10 of 14 against 9.5. That verdict is the Agentic Index coverage score, graded from each vendor's own published materials.
Causely infers root cause and blast radius from a causal model of the environment, for SRE teams and for their own AI agents through an MCP server and a GraphQL API; Professional is 2,000 dollars a month for up to 500 services, with a 30 day trial. 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 from 100 dollars a month. Causely is Full on human approval, knowledge grounding and prebuilt agents, where Cleric is Partial; Cleric is Full on memory and Partial on model choice and evaluation, where Causely is None. Choose Causely for causal root cause that your own agents can query; choose Cleric for verification after merge with memory.
On the Agentic Index production ops ranking, neither Causely nor Cleric clears the bar, which asks for all five resolution loop capabilities documented in full. Causely does not document workflow orchestration in full; Cleric does not document workflow orchestration in full, nor human oversight and guardrails. 14 of the 37 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. Causely and Cleric 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 955 researched vendors. No vendor pays for placement and no vendor has reviewed this page. How this evidence is graded
Choose Causely if
- Root cause and blast radius should come from a causal model of your environment.
- Your own AI agents should query the analysis through MCP or GraphQL.
- A flat price by service count, 2,000 dollars a month for up to 500 services, fits your budgeting.
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 lower entry price, from 100 dollars a month in credits.
| Feature | C Causely |
C Cleric |
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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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CauselyIntegrations & Tool Calling Telemetry comes from a named catalog of 20+ sources (Datadog, Dynatrace, Prometheus, Splunk, Elasticsearch, AWS, Azure, GCP, Kubernetes, Snowflake), and outbound workflows reach Slack, Microsoft Teams, incident.io, Splunk On-Call and a generic webhook. The executor on the in-cluster mediator applies scaling actions in the customer's cluster. Sourcedocs.causely.airead 2026-09-29 |
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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 |
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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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CauselyWorkflow Orchestration A causal reasoning engine produces root cause and blast radius in one analysis, and there is no stated multi step workflow builder or multi agent runtime. The six MCP skills sequence tool calls inside the customer's own agent, so that orchestration belongs to the agent, not to Causely. Sourcedocs.causely.ai/agent-integration/mcp-serverread 2026-09-29 |
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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 |
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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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CauselyTriggers & Channel Coverage Analysis runs continuously on streaming telemetry and on alerts routed in from existing tools ('An alert fires, Causely answers why'), and diagnoses are pushed to Slack, Teams, incident.io, Splunk On-Call or a webhook with nobody typing. Sourcedocs.causely.airead 2026-09-29 |
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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 |
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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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CauselyKnowledge Grounding & RAG Causely maintains a causal model of the customer's environment. The mediator discovers services, infrastructure and dependencies, and agents periodically forward topology and symptom data to the backend, which keeps the causal graph current and queryable. Sourcedocs.causely.ai/getting-started/architectureread 2026-09-29 |
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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 |
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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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CauselyMemory & State Persistence What persists is application data, not agent memory. Diagnosis history is kept under 30 day retention, and thresholds, service tiers, custom causes and snoozed issues are settings the product applies. The causal model is the product's picture of the environment, and there is no stated separate memory layer. Sourcedocs.causely.ai/getting-started/architectureread 2026-09-29 |
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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 |
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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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CauselyHuman Oversight & Guardrails Remediation commits only when a person triggers it from the UI, either as automated remediation or as a guided fix applied with one click, and automated actions require the executor to be enabled on the mediator in that cluster. There is no stated way for remediation to run without a person. Sourcedocs.causely.ai/in-action/automate-remediationread 2026-09-29 |
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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 |
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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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CauselySecurity, Identity & Governance Access is managed through workspace SSO, user management with assigned roles, personal and tenant wide API tokens, and MCP configuration tools limited to Developer and Admin roles, backed by an admin audit log. Causely holds SOC 2, with the report available on request, and encrypts data in transit and at rest. Sourcecausely.ai/securityread 2026-09-29 |
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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 |
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Observability & Auditability Traces, logs, execution histories, metrics, audit events, and debugging detail for production agent behavior. |
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CauselyObservability & Auditability Each diagnosis carries its causal chain and the targeted telemetry sent as evidence, and remediation keeps an auditable action history tied to the entity it changed, a per action record readable after the fact. Sourcedocs.causely.ai/in-action/automate-remediationread 2026-09-29 |
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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 |
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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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CauselyDeployment & Data Residency The mediator and agents always run in the customer environment, the causal engine runs in the customer's cloud (BYOC) or in Causely managed infrastructure, and Enterprise offers on premises and air gapped deployment. Raw telemetry stays local. Causely publishes no region list. Sourcedocs.causely.ai/getting-started/architectureread 2026-09-29 |
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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 |
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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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CauselyPrebuilt Agents, Templates & Packs Six named skills ship with the MCP server, each with a distinct job. Three handle live incidents (causely-alert-triage, causely-correlated-incidents and causely-k8s-investigation), and the other three cover change impact (causely-change-impact), health reporting (causely-health-reporting) and postmortems (causely-postmortem). Sourcedocs.causely.ai/agent-integration/mcp-serverread 2026-09-29 |
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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 |
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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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CauselyModel Flexibility & Routing Diagnosis comes from a deterministic causal reasoning engine, with no stated LLM provider or customer model choice. Customers can bring their own agent through the MCP server, which extends the product and is not a choice of model. Sourcecausely.airead 2026-09-29 |
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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 |
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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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CauselyAPIs, SDKs & MCP Extensibility A GraphQL API at api.causely.app/query takes bearer token authentication with personal and tenant API tokens, and comes with query guides for defects and root causes, hiding root causes, and SLOs on paths. An MCP server at api.causely.app/mcp adds 45 tools, including configuration of thresholds, service tiers and snoozing. Sourcedocs.causely.ai/api/graphql-clientsread 2026-09-29 |
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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 |
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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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CauselyTesting, Debugging & Optimization There is no stated evaluation harness, scored tests or quality gate for the diagnosis engine. Post deploy validation compares the customer's services before and after a release, which tests the customer's change and not the agent, and the 63 percent benchmark is Causely's own marketing figure. Sourcedocs.causely.ai/agent-integration/mcp-serverread 2026-09-29 |
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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 |
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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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CauselyBrowser & Computer Use Causely works through telemetry sources, the in-cluster executor, a GraphQL API and an MCP server, with no stated browser, desktop or computer control. Sourcedocs.causely.airead 2026-09-29 |
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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 |
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Pricing snapshot
Sourced from the Index pricing dataset · open each vendor's profile for full detail.
| Pricing | C Causely |
C Cleric |
|---|---|---|
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Entry price Lowest public entry point |
Professional at $2,000 per month for up to 500 services. Enterprise is custom. | From $700 per month on Team, with 1,400 credits. Pro is $2,000 and Enterprise is custom. |
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Pricing confidence How public the numbers are |
Public, exact | Public, exact |
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Billing Primary billing axis |
Billed monthly by service count on Professional, up to 500 services. Enterprise runs on a custom plan. | 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. |
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Variable cost Workload / overage exposure |
Low variable cost | Medium variable cost |
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Free tier / trial Try before you buy |
No free tierTrial
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No free tierTrial
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Buying motion Self-serve vs sales call |
Mixed | Mixed |
Both publish prices: Causely by service count, Cleric by credits. Model your own alert and service volumes before comparing.
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