Quick answer#
Kore.ai Artemis merits an enterprise pilot when agent governance, structured workflows, evaluation and traceability matter more than a low-friction personal assistant. It is a real, documented platform, but its claims of “zero unauthorized agent actions” and “no surprises in production” are vendor claims, not results we independently reproduced.[3][5]
Verified October 3, 2026. We did not verify public numeric Artemis prices. Its own FAQ describes Free, Team, Business and Enterprise billing models, so “everything is quote-only” would also be inaccurate. Obtain the account-specific rate card and entitlements before estimating a deployment.[4]
- Best for: an enterprise team with engineering, security and procurement ownership, a defined workflow, and the ability to run an acceptance pilot.
- Avoid if: you need a transparent all-in price immediately, a simple personal agent, or independently proven production outcomes without conducting your own evaluation.
- Our recommendation: shortlist it for the control architecture, then make purchase approval conditional on measured task success, isolation, recovery, cost and export tests.
This is a research-based buyer review. We inspected official product material, public documentation, current release notes, general terms, third-party review material and an ad preview. We did not access an Artemis tenant, buy a contract or run a production benchmark. This site promotes Hermes and managed hosting; neither should be represented as an equivalent enterprise control platform.
Identity: Artemis is not every Kore.ai product#
The official page calls Artemis the Kore.ai Agent Platform and describes three named components: Agent Blueprint Language (ABL), Arch and Autoloop.[3] The public release notes record v1.0 on May 21, 2026 and subsequent releases through v1.7.1 on September 29.[5] These are stronger identity and freshness signals than a third-party roundup with an undated price.
ABL is described as a typed, schema-driven way to define agent behavior, tools, guardrails, orchestration and handoffs. Arch translates plain-language intent into an agent system and ABL. Autoloop is the advertised continuous testing, diagnosis and improvement layer.[3] Those names identify the offer under review; they are not independently verified performance outcomes.
Do not substitute AI for Service, Contact Center AI, older XO Platform prices, or another vendor's product called Artemis. Even review directories can mix generations. The TrustRadius page uses an Artemis title while its description still explains the Experience Optimization (XO) Platform.[13] That matters when someone cites an old rating as proof of the new product's reliability.
For a less enterprise-heavy visual automation purchase, the n8n AI Agent Builder review covers a different architecture and billing model rather than pretending the two are interchangeable.
Pricing: known billing models, unknown numeric rates#
The Artemis FAQ publishes these plan descriptions:[4]
- Free: no platform charge, with usage limits.
- Team: a per-seat monthly subscription.
- Business: per-seat plus usage-based tokens and messages.
- Enterprise: custom pricing and an annual contract.
We did not find numeric rates or the free-tier quotas in the retrieved FAQ. The public product page leads to a demo, and the /pricing page returned a not-found page during this review. We did not create an account or inspect an authenticated checkout. Those boundaries prevent an honest dollar estimate, not an honest review.
The FAQ says each workspace has its own billing. It describes a session as a conversation from first message to end, says Studio test sessions also count, and says sessions are billed regardless of message count.[4] Elsewhere the same FAQ describes Business using tokens and messages. These statements may concern different billing dimensions; they are not enough to reconstruct an invoice.
Ask for a written matrix showing which meter applies to your edition: seats, conversations, tokens, messages, voice, evaluation runs and platform minimums. Do not import a 15-minute session rule from another Kore.ai product without an Artemis-specific contractual reference.
For cross-product budgeting, our AI-agent pricing guide separates software, inference and operations. It does not supply the missing Artemis rate card.
Hidden running costs and quota behavior#
The FAQ supports adding your own model credentials and displays token consumption by provider and model.[4] A platform quote must therefore say whether model charges are included, passed through, or paid directly to the model provider. Do not add or remove inference costs based only on a salesperson's monthly platform headline.
Budget requests should cover:
- Builder seats and workspaces: which users are billable, which roles are included, and whether separate development or business workspaces create extra charges.
- Production and test sessions: Studio tests count toward usage according to the FAQ; ask how automated evaluations are metered.[4]
- Model usage: retries, long context, multiple agents and evaluation judges can affect consumption. Get provider billing and platform accounting reconciled in the pilot.
- Knowledge and connected services: request explicit prices for ingestion, storage, search, external APIs and any premium connectors you require.
- Voice: verify the telephony, speech recognition, synthesis and realtime-model components of an end-to-end call, rather than assuming a text session rate covers them.
- Implementation and operations: name the owners of data permissions, monitoring, regression tests, training, incident handling and changes to business policies. Request professional-services and support scope separately.
The FAQ says an email warning is sent at 80% of session or token quota; above 100%, new sessions pause until an upgrade or the next cycle, while existing active sessions are not interrupted.[4] That is an important availability question for a customer-facing deployment. Ask whether the negotiated Enterprise contract changes the behavior, and demonstrate the boundary in a safe environment.
The FAQ also describes immediate, prorated upgrades.[4] Proration on an upgrade is not a refund promise on cancellation. Avoid conflating those two different financial events.
Features: inspect the control architecture#
Artemis combines default model reasoning with optional structured FLOW steps. The FAQ says a per-step reasoning flag controls whether the model reasons at a given step.[4] That is a useful distinction: “agentic” need not mean allowing an LLM to choose every side effect.
The vendor advertises compiled intermediate representation, explicit memory contracts, typed trace events, cycle detection and policy enforcement outside the LLM.[3] During a technical evaluation, ask the team to show the mechanism behind those claims, not just a successful conversation.
Useful demonstrations include:
- A forbidden tool action rejected by the runtime, with a trace explaining why it did not execute.
- A structured flow requiring missing information instead of inventing it.
- A human approval that pauses and resumes correctly after a timeout or denial.
- A model change followed by a regression evaluation, not merely a successful deployment.
- A rollback that restores behavior while keeping the relevant audit evidence.
The FAQ documents session traces containing agent, LLM, tool and step spans, as well as deployment rollback to a previous version.[4] That documentation makes “there is no rollback” too broad a criticism to publish without current reproduction. Documentation still is not proof the feature satisfies your recovery objective.
If your uncertainty is what an agent can realistically do, learn AI by shipping with an agent is a smaller learning loop. It is not a replacement for enterprise security review or expert-led compliance training.
Fresh release notes: the staging-data caveat#
The September 29 v1.7.1 release notes contain a concrete purchasing issue: Agent Tables can now be used from non-production deployments, but all environments currently read and write the same production rows; per-environment table data is not yet available.[5]
A label such as “staging” is therefore not sufficient evidence of data isolation for this feature. Before allowing a pilot to write to an Agent Table, establish exactly which rows it can alter. Use non-sensitive test data and an isolation design confirmed by the vendor. This is documented behavior, not an allegation of a breach.
Other recent notes show active development. The September 27 v1.7.0 release expands evaluation suites to 500 scenarios and 10 personas while Arch-generated suites still start at three of each. It adds unified Human Task handling and more detailed LLM credential attribution.[5] A larger maximum is useful only if your team actually builds a representative evaluation set.
The September 29 notes also say agent-spoken handoff announcements, no-input prompts and farewells now appear in transcripts where they previously did not.[5] That history is relevant to audit acceptance: confirm that the tenant version you evaluate includes the behavior you rely on.
Security claims, observability and responsibility#
The sales page says “100% of AI interactions audited.”[3] The FAQ separately documents a default trace-sampling rate of 1.0 and suggests reducing it to 0.1 or 0.01 in high-traffic environments, with error-based sampling.[4] These may describe different audit and tracing layers. They should not be silently collapsed into a guarantee that every event is always retained.
Ask which records are mandatory audit events, which are sampled diagnostics, how long each persists, who can export or delete them, and whether secrets or personal information can enter those records. The September 29 notes specifically describe runtime secret resolution for workflow tools without putting secret values into model prompts or traces.[5] Verify the exact tool path and configuration rather than assuming every integration inherits that property.
Model behavior remains a separate risk from orchestration correctness. A compiler may validate a flow while the model still misunderstands a customer, retrieves the wrong document, or produces an inaccurate answer. Require business-level acceptance cases as well as syntax checks.
Our MCP security guide explains why a connector's permissions need explicit review. The principle is useful across platforms, but Hermes-specific settings are not Artemis configuration instructions.
Cancellation, refunds and contract questions#
Kore.ai's general Terms of Service identify Kore.ai, Inc. and display an April 24, 2025 update date. Their paid-services section says amounts paid are non-refundable and charge disputes must be raised within 30 days of billing.[12]
These are broad service terms with messaging and bot language, not an Artemis-specific negotiated order form. We did not verify an Artemis satisfaction guarantee, trial-to-paid conversion policy, cancellation notice window, early-termination right or contract-level refund remedy. Ask for the actual subscription agreement, order form, support schedule and data-processing terms before committing.
Your procurement checklist should request:
- Initial term, renewal mechanism and deadline for giving non-renewal notice.
- Minimum spend, overage rates and changes to price at renewal.
- What happens to a paid pilot if acceptance criteria are not met.
- Remedies for service failures and any exclusions from service credits.
- Export access and deletion timing after termination.
- Whether a statement in the proposal overrides or merely supplements general website terms.
Do not assume stopping usage stops an annual contract, or that a free tier establishes a right to a paid-plan refund.
Is Kore.ai Artemis legit? Complaints and independent evidence#
The identified supplier, detailed public platform documentation and dated release history support treating Artemis as a real product worth evaluating.[3][5][12] That conclusion is narrower than accepting “five times faster” or “zero production surprises.” We did not verify independent benchmarks supporting those absolute or comparative marketing claims.
TrustRadius supplies useful but limited buyer context. The retrieved profile includes praise for chatbot development, enterprise capabilities and customer journeys; visible cons include pricing, updates, session management and statistics.[13] Because the same page retains XO descriptive material under an Artemis heading, we do not present these as a clean sample of Artemis-only buyers.
Nor do we copy a directory's overall rating into Review schema or claim a measured complaint frequency. Older platform experience can suggest pilot questions, but cannot establish that a particular current Artemis release has the same defect. We did not find a clearly attributable independent Artemis benchmark in the searches performed.
The practical response to uncertainty is not to label the product a scam. It is to require reference customers using the same edition, deployment arrangement, channels and scale that you intend to buy.
Advertising: brand proof is not performance proof#
The live Google Ads Transparency preview for Kore.ai, Inc. displayed “Comparing AI Platforms? - See How They Compare” and the copy “Six analyst evaluations in 2025-2026. Kore.ai holds the Leader position in all six.” The checked creative was CR10605939678932631553 under advertiser AR01074269177264472065, last shown October 2, 2026.[17]
The current preview labelled it Text, not Video, correcting the older cached export's format classification. We did not verify a YouTube placement. This was a Kore.ai brand creative, not an Artemis-specific performance test; its analyst-leadership statement remains an advertising claim unless the relevant reports are separately inspected. We did not infer spend, audience, conversion rate or purchase quality from the ad.
Pros, cons and alternatives#
Pros: documented structured and reasoning modes, an explicit control architecture, session-level debugging, evaluation tooling, rollback documentation, and active release maintenance.[3][4][5] These are reasons to run a serious enterprise pilot.
Cons: unverified numeric rates, contract-dependent economics, a learning and integration burden, mixed-generation third-party reviews, and a specific shared-table-data limitation across environments.[4][5][13] Absolute marketing claims deserve more scrutiny than feature lists.
If you mostly need hosted actions across business apps, Zapier Agents is a different, smaller purchase to examine. If deterministic visual workflows are central, evaluate n8n rather than paying for a wider enterprise platform by default. For an enterprise contact-center or cloud-platform purchase, obtain competing proposals against the same acceptance criteria instead of comparing mismatched per-seat and per-session headlines.
Hermes is a candidate when the actual requirement is a configurable personal action agent. Our Hermes review covers that fit; install it yourself if you want operating control, or examine managed cloud hosting if maintaining infrastructure is the obstacle. Those paths do not automatically replace Artemis's enterprise governance, organizational support or commercial assurances.
Decision checklist: make the pilot earn the contract#
- Name one business process, its owner, the target users and the forbidden actions.
- Get a written rate card covering every production, test, model, voice and support meter.
- Confirm the platform edition, tenant version and contractual deployment commitments.
- Test adversarial requests, permission denials, missing data and model or tool outages.
- Verify Agent Table isolation before any non-production write touches real data.
- Measure successful outcomes and human correction effort, not just fluent responses.
- Reconcile trace, audit and billing records; document what sampling omits.
- Export the agent definitions and session evidence, then demonstrate rollback and termination access.
Bottom line: Artemis's documented architecture warrants evaluation for demanding enterprise workflows. Its price, implementation effort and guarantees must be established in your own pilot and contract, not borrowed from another Kore.ai SKU or inferred from an ad.
Sources#
[3] https://www.kore.ai/ai-agent-platform — Kore.ai Artemis official product [4] https://docs.kore.ai/agent-platform/faq — Artemis FAQ and billing models [5] https://docs.kore.ai/agent-platform/release-notes/recent-updates — Artemis release notes [12] https://www.kore.ai/terms-of-service — Kore.ai general Terms of Service [13] https://www.trustradius.com/products/kore-ai — TrustRadius mixed-generation Kore.ai profile [17] https://adstransparency.google.com/advertiser/AR01074269177264472065/creative/CR10605939678932631553 — Google ad preview: Kore.ai Inc.