Support teams that run on B2B ticketing tools have a specific problem with AI agents. The tool is right for the business: conversations arrive in shared Slack Connect channels, Microsoft Teams channels, email threads and in-app chat rather than a single web widget, and the product was chosen because it understands accounts. Then the AI agent evaluation starts, and half the shortlist turns out to be a helpdesk with an agent attached. Adopting it means moving the queue.
The pressure to adopt something is real. In a 2026 Gartner survey, 91% of customer service leaders reported executive pressure to implement AI in 2026.
This comparison looks at eight AI support agents through one lens: what happens to the ticketing system you already run. It separates vendors built as a layer over whatever queue you have from vendors whose AI is a feature of their own queue. The audience is B2SMB, meaning B2B companies whose support volume behaves like consumer support: thousands of tickets a month from small business customers rather than a few hundred from named enterprise accounts.
Key Takeaways
The real question is architecture, not integration count. A vendor with fifty named connectors can still be ticketing-first. Ask how a new, unnamed ticketing system gets connected, and whether it needs the vendor's engineers to ship code.
"Sits on top" has a concrete definition. At minimum the agent needs inbound webhooks, an agent seat or API identity to reply as, the ability to reassign to a human, and write access to tags, status and custom fields.
Single-vendor and layered are both defensible. If your ticketing vendor has shipped credible agentic support, using it is the shortest path and removes a contract. The trade is that your AI ceiling is set by that vendor's roadmap.
B2SMB support breaks the usual B2B assumptions. With tens of thousands of small business customers, volume and repeatability look like consumer support, so AI resolution rates behave like consumer numbers.
Resolution quality beats deflection rate. In shared channels a wrong answer is visible to the customer's whole team, so CSAT and escalation quality beat containment as signals.
Voice and SMS usually sit outside the layering story. Most vendors here connect to ticketing through generic webhooks but handle telephony through named integrations. Scope those separately.
What to Look For in an AI Agent That Layers Onto an Existing Helpdesk
The first thing to test is how the vendor handles a ticketing system it has never heard of. Most AI support vendors publish a connector list, and that list answers a different question than the one you are asking.
The second is what the AI agent does to your operational data. An AI layer that replies but cannot tag, set status or write custom fields will quietly break every report your team runs.
The third is escalation. In B2B and B2SMB support the escalation path is not "hand to the next available agent". It is "hand to the account owner, or the engineer on call, or into the shared channel the customer's team is already watching". A layered agent has to reassign inside your routing rules, which means it needs the ticketing system's assignment and group APIs, not only its messaging APIs. For criteria that apply regardless of which queue you run, see our roundup of the best AI customer support software.
The 8 AI Support Agents at a Glance
# | Vendor | Best for | Keeps your existing ticketing? |
|---|---|---|---|
1 | Lorikeet | B2SMB teams with multi-step workflows that want to keep their queue | Yes, including systems with no named connector |
2 | Pylon Agentic Support | Teams on Pylon that want one vendor and one contract | Only if your queue is Pylon |
3 | Decagon | High-volume teams on a mainstream helpdesk | Yes, where a connector exists |
4 | Intercom Fin | Teams on Intercom, or wanting Fin over another queue | Partly, depending on the deployment mode |
5 | Maven AGI | Enterprise teams with many systems to reach | Yes, where a connector exists |
6 | Sierra | Large brands running voice and chat together | Yes, scoped during implementation |
7 | Zendesk AI | Teams standardized on Zendesk | Only if your queue is Zendesk |
8 | Plain | Technical B2B teams choosing a new queue outright | No, it is the queue |
How These Were Selected
Every vendor here is in production with support teams handling real ticket volume, and each has an AI agent shipped today. Vendors whose AI only suggests replies for human agents were left out.
The ordering reflects one criterion above the others: how little of your existing setup you have to give up. That favors AI-layer-first vendors and penalizes ticketing-first ones, a deliberate bias stated openly rather than hidden in the entries.
Capability claims come from each vendor's public materials and are hedged accordingly. Compliance posture, hosting and contractual scope vary by plan and deployment, so buyers should validate specifics with the vendor.
AI-Layer-First vs Ticketing-First: What "Sits on Top" Actually Requires
Vendors split cleanly into two architectures, and almost every disappointing evaluation comes from a buyer who did not notice which one they were looking at.
Ticketing-first vendors own the queue. The AI agent is a capability of that queue, with complete access to conversations, accounts, routing and reporting because all of it is one product. Zendesk AI, Pylon Agentic Support and Plain are ticketing-first. The cost is that adopting the AI means adopting or keeping the queue.
AI-layer-first vendors treat the queue as an external system. Lorikeet, Decagon, Sierra and Maven AGI are built this way, and Intercom positions Fin partly this way while also being a ticketing vendor.
An AI agent that genuinely sits on top of a ticketing system needs five things:
Inbound events. A webhook that fires when a conversation is created and when a customer replies, carrying enough payload to identify the account and thread.
An identity to reply as. An agent seat or API identity, so the reply lands in the thread as a normal message.
Reassignment. Handing the conversation to a human, group or account owner through the ticketing system's own assignment rules.
Write access to metadata. Tags, status, priority and custom fields, so existing reports and SLAs keep working once the AI takes volume.
Read access to history. Prior tickets from the same account, so a follow-up is not answered as a first contact.
When those five exist, the ticketing system is largely interchangeable from the agent's perspective. When one is missing, the gap shows up as a specific failure: no tags means broken reporting, no reassignment means no clean escalation, no history means repetitive questions. Teams on Kustomer or Front can see this in practice in our guide to the best AI support agents for Kustomer and Front.
1) Lorikeet
Best For: B2SMB support teams with multi-step workflows that want an AI agent without changing the ticketing system they already run
Fit: Designed as a layer over existing ticketing rather than as a replacement queue; Lorikeet states SOC 2 Type 2, DPA and BAA availability including HIPAA, and buyers should validate contractual and technical scope for their deployment
Pricing: Published plans from $2,100/mo paid annually on Start and $5,100/mo on Scale, with per-resolution pricing on top and custom pricing on Signature
Lorikeet is an AI customer support agent for companies whose support involves policy, money, identity and multi-system workflows rather than article lookup. The choice that matters here is that Lorikeet has never shipped its own ticketing system and does not intend to. It is the reasoning and action layer on top of whatever queue a team already runs, which makes the integration surface a first-class part of the product.
That produced a capability called the Flexible Ticketing System. Rather than requiring a bespoke connector for every helpdesk, Lorikeet connects to any webhook-based ticketing system through a generic configuration that Lorikeet's team sets up with you during implementation. It is in production today with subscribers running on systems for which Lorikeet wrote no integration code, including one team whose entire support operation runs through Gmail with no ticketing product underneath it. Alongside that path, Lorikeet ships named connectors for Zendesk, Intercom, Salesforce, Kustomer, Front and HubSpot, plus a custom API route.
Key Features
Flexible Ticketing System. Connects webhook-based ticketing systems without a bespoke connector, configured with Lorikeet's team. It covers digital channels and does not extend to voice.
Named ticketing connectors. Zendesk, Intercom, Salesforce, Kustomer, Front and HubSpot, plus a custom API path.
Multi-step workflow execution. The agent calls internal and third-party APIs to complete work end to end, with one-time passcode identity verification before any account-changing action.
Native voice and SMS. Number provisioning, inbound and outbound calling and messaging on one number, and call routing that can replace an IVR, on Lorikeet's own telephony rather than the ticketing layer.
Business hours and after-hours routing. Per-channel settings for when the agent handles a channel, plus custom after-hours behavior.
Auditability and simulation. Interaction-level traces of what the agent did and why, with testing against historical tickets before release.
Why It Made the List
Lorikeet ranks first because it is the only vendor here whose answer to "we run a ticketing system you have never integrated with" is a shipped feature rather than a roadmap conversation. A connector list is the right answer when your queue is on it, and for a large share of B2B teams the queue is not on anyone's list.
The published results come from teams whose support behaves like consumer support despite selling to businesses. Magic Eden, the NFT marketplace, published that within the first month of rolling out Lorikeet it reached 74% CSAT, roughly 30 percentage points above the AI agent it used previously and four points off its human agent scores. Flex, which lets renters split monthly rent payments, published that it tested AI vendors head to head before selecting Lorikeet and reported twice the CSAT of its previous support tool and a 50% cut in median time to resolution, while absorbing four times normal chat volume during rent week.
The limits are worth stating. Flexible Ticketing System is configured with Lorikeet's team rather than by you in an afternoon, so it adds implementation time, and it does not cover voice, which is scoped separately. And Lorikeet is a layer, so if you want one vendor and one bill for both the queue and the AI, this is the wrong shape of product. You can book a demo to test the configuration against your setup.
2) Pylon Agentic Support
Best For: Teams already on Pylon that want their AI agent from the same vendor, on the same contract
Fit: Ticketing-first; Pylon states enterprise security controls, and buyers should confirm certifications, hosting and data handling for their plan
Pricing: Pylon publishes plan tiers for its support platform; AI capabilities are quoted based on scope
Pylon is a support platform for B2B companies that run support through shared channels. It handles Slack Connect and Microsoft Teams as first-class surfaces alongside email, in-app chat and a customer portal, and models accounts rather than standalone contacts. In 2026 Pylon extended the platform with its own agentic support, so the AI runs natively inside the product that holds the conversations.
Key Features
Shared-channel support as a native surface. Slack Connect and Microsoft Teams conversations handled as tickets without a bolt-on bridge.
Account-centric data model. Conversations, contacts and issues roll up to accounts, which matters for B2B reporting.
Native agentic support. AI that answers and acts inside Pylon, with the same account context human agents see.
Knowledge base and customer portal. First-party help content the AI can draw on without a separate knowledge integration.
Workflows, routing and engineering escalation. Assignment designed around account ownership, with connections into issue trackers and CRM.
Why It Made the List
Pylon is a good product, and this entry is a genuine recommendation rather than a courtesy. If your team is already on Pylon and the question is "how do we add AI", starting with Pylon's own agentic support is the sensible first move.
The trade is a ceiling. The depth of what the AI can do is bounded by that vendor's AI roadmap and by how much engineering attention goes to the agent versus the queue. Teams whose support involves long multi-system workflows or identity verification before account actions should test a Pylon-native agent against a dedicated one on their hardest tickets. Lorikeet does not have a named Pylon connector and does not claim one; it has the generic webhook path described above, which is what a Pylon team would evaluate if it wanted a dedicated AI layer while keeping its queue.
3) Decagon
Best For: High-volume B2SMB and consumer teams on a mainstream helpdesk that want a dedicated AI layer
Fit: AI-layer-first; Decagon states enterprise security and compliance coverage, which buyers should confirm for their deployment
Pricing: Enterprise pricing, typically structured around resolution volume
Decagon builds AI agents across chat, email and voice, designed to sit alongside an existing support stack rather than replace it. Its published customer base skews toward high-volume consumer and marketplace businesses, the volume profile many B2SMB teams share.
Key Features
Named helpdesk connectors. The agent replies and escalates inside the existing queue on supported platforms.
Multi-channel coverage. Chat, email and voice from the same agent configuration.
Natural-language agent rules. Behavior specified in plain language rather than only through flow builders.
Action execution through APIs. The agent calls connected systems to complete tasks rather than only answering.
Analytics and context-preserving handoff. Reporting on topics and escalation patterns, with history attached on transfer.
Why It Made the List
Decagon is one of the clearest examples of the AI-layer-first architecture, and a strong option for teams whose ticketing system is on its connector list.
The limitation here applies to most connector-based vendors. If you run Pylon, Plain or an internal queue, you are asking for engineering work on the vendor's side, which is a reasonable ask at enterprise deal sizes and an unreasonable one below them. Teams weighing Decagon against other dedicated layers in a regulated context can compare the approaches in our Sierra vs Decagon vs Lorikeet breakdown.
4) Intercom Fin
Best For: Teams already on Intercom, and teams elsewhere that want Fin's answer quality without moving their queue
Fit: Both architectures at once; Intercom states HIPAA attestation and BAA availability for applicable customers, and buyers should confirm product and workspace scope
Pricing: Intercom publishes per-resolution pricing for Fin on top of Intercom platform seats
Fin is Intercom's AI agent, unusual here because Intercom is a ticketing vendor that also markets its agent for other helpdesks. Outside it, Intercom positions Fin as deployable over certain other support platforms and through an API, so the classification depends on which deployment you buy.
Key Features
Knowledge-grounded answers. Responses drawn from help center content, past conversations and connected sources.
Deployment over other helpdesks. Intercom states Fin can run on top of certain third-party support platforms as well as inside Intercom.
Actions through connected systems. Fin can take defined actions rather than only answering.
Per-resolution pricing. Published pricing tied to resolutions rather than conversation count.
Escalation with context. Handoff inside the Intercom inbox or the connected platform, across chat, email and messaging.
Why It Made the List
Fin has the largest deployed footprint of any AI agent in this category and sets the baseline most buyers compare against, with per-resolution pricing that is useful for budgeting.
Two caveats matter. Third-party helpdesk support is a specific list rather than a general capability, so the usual question applies: is your queue on it. And Fin's design center is knowledge retrieval with actions attached, which suits support where most tickets have a documented answer. Teams whose hardest tickets require sequenced API calls and verification steps should test those tickets, using an approach like the one in our comparison of AI support agents for multi-step fintech workflows.
5) Maven AGI
Best For: Enterprise teams with many systems to reach and a preference for a large connector catalog
Fit: AI-layer-first with explicit overlay positioning; Maven states a broad certification set including SOC 2 Type II, ISO/IEC 27001 and HIPAA/HITECH with BAA availability, which buyers should scope to their deployment
Pricing: Enterprise pricing
Maven AGI markets an enterprise agent platform running one reasoning engine across chat, email, voice and messaging, and describes its architecture as an overlay that connects to an existing stack rather than requiring a helpdesk replacement. Maven states a catalog of more than a hundred integrations across support, CRM, data and contact center systems.
Key Features
Single reasoning engine across channels. The same knowledge and policy logic applied to chat, email, voice and web.
Large pre-built connector catalog. Integrations across support, CRM, data and contact center categories.
Enterprise compliance posture. A broad published certification set, which buyers should map to the products they license.
Real-time sensitive-data redaction. Applied to voice and text.
Risk-aware escalation and governed actions. Confidence thresholds that route to humans with context, plus record updates and approvals.
Why It Made the List
Maven is a credible overlay vendor and is explicit about not wanting to replace your helpdesk, which is rarer than it should be. For an enterprise buyer with a sprawling system landscape, the catalog shortens implementation.
It sits at five for one reason. A hundred connectors is a strong answer to "do you support my stack" and a weak answer to "do you support it when it is not on your list". Maven's deal sizes point toward larger enterprises, and B2SMB teams on a newer B2B ticketing product should confirm early whether their queue is in the catalog.
6) Sierra
Best For: Large consumer brands running voice and chat together with a dedicated implementation team
Fit: AI-layer-first; Sierra states enterprise security and compliance coverage, which buyers should validate for their deployment
Pricing: Enterprise pricing, commonly structured around outcomes
Sierra builds conversational AI agents for large brands, with emphasis on voice alongside digital channels and on agents that reflect a company's voice and policies.
Key Features
Voice and digital in one agent. Phone and chat from the same agent definition.
Brand-and-policy configuration. Agent behavior shaped by explicit company rules and tone.
Integration built during implementation. Connections to support, commerce and internal systems scoped per customer.
Outcome-oriented commercial model. Pricing commonly tied to resolved outcomes.
Supervision and escalation tooling. Monitoring with correction, and handoff into the existing support operation.
Why It Made the List
Sierra's implementation-led model means it can in principle connect to an unusual ticketing system, because integration is scoped per customer anyway. For a team on Pylon or Plain with enterprise budget, that is a legitimate path.
It sits at six for fit rather than capability. Sierra's centre of gravity is large consumer brands with substantial voice volume and enterprise contract sizes. A B2SMB team with a few thousand tickets a month is not the profile it is optimized for, and the implementation-led model that makes unusual queues possible also makes them expensive.
7) Zendesk AI
Best For: Teams standardized on Zendesk that want AI without adding a vendor
Fit: Ticketing-first; Zendesk states that covered services under its BAA support HIPAA compliance, and buyers should confirm which products are included
Pricing: Published per-seat plans, with AI sold as add-ons or included at higher tiers
Zendesk AI covers AI agents, agent assistance, routing and knowledge features inside the Zendesk platform. For teams already on Zendesk it is the default option, with native access to tickets, customer context and routing.
Key Features
Native access to tickets and context. No integration layer between the AI and the queue.
AI agents and agent assistance. Both autonomous handling and copilot features.
Intelligent routing and triage. Classification and assignment inside existing Zendesk rules.
Knowledge management. Help center content and content-gap suggestions in the same product.
Voice and digital channels, with published pricing. Contact center features inside the platform, and costs available without a sales call.
Why It Made the List
For a Zendesk team, native AI removes an integration and a contract.
It ranks low here for a mechanical reason: it requires Zendesk. A team on Pylon evaluating Zendesk AI is evaluating a migration, which is a larger decision than choosing an AI agent. Teams already on Zendesk comparing native AI against a dedicated layer can read our guide to the best AI agents for Zendesk.
8) Plain
Best For: Technical B2B teams choosing a new support platform outright, with AI as part of that platform decision
Fit: Ticketing-first; Plain states security controls appropriate to B2B SaaS support, which buyers should confirm for their plan
Pricing: Published plan pricing
Plain is a support platform for technical B2B companies, with email, Slack, Microsoft Teams and in-app chat in one workspace and a developer-friendly API at the centre. It shares Pylon's premise that B2B support happens in shared channels and is organized by account.
Key Features
Multi-surface B2B support. Email, Slack, Microsoft Teams and in-app chat in one queue.
API-first design. A developer-oriented API for customizing how support data flows.
Account-level context. Customer cards and account history available to agents.
AI assistance and automation. AI features layered into the platform's workflow.
Engineering escalation and published pricing. Issue tracker connections for bugs that leave support, with plan costs listed publicly.
Why It Made the List
Plain is a well-built product for the teams it targets. It sits last here purely because of the lens: if you already run a queue you like, adopting Plain means replacing it.
Plain becomes the right answer for a team unhappy with its queue anyway that wants one decision instead of two. Evaluating Plain and Pylon as platforms, with AI as one factor among several, is cleaner than bolting an AI layer onto a queue you were going to leave.
How to Choose
Six criteria separate the options above for a team that wants to keep its ticketing system.
Ask the unnamed-system question first. Put your exact ticketing system to each vendor and ask what connecting to it involves. Answers divide into "we have a connector", "we would build one" and "we have a generic path". The third is the only one that does not depend on your deal size.
Test the five layering requirements in your own instance. Inbound events, reply identity, reassignment, metadata writes and history reads. A pilot that only demonstrates replying does not test the parts that break.
Score on your hardest tickets. Every vendor here handles password resets. The difference shows up on the ticket that needs three API calls, a policy decision and a verification step.
Separate the voice decision from the ticketing decision. Ticketing connectivity and telephony are different integration surfaces at nearly every vendor here.
Price the migration you are avoiding. If a ticketing-first vendor is cheaper on paper, add the cost of moving the queue: reporting history, integration rebuilds, retraining and weeks of degraded service.
Check who configures what. Some integration work is self-serve and some requires the vendor's implementation team. The difference changes your timeline by weeks, so get it in writing.
Deeper Feature Comparison
The matrix below is scoped to the layering question rather than general capability, and includes gaps in our own product as well as others.
Capability | Lorikeet | Pylon | Decagon | Intercom Fin | Maven AGI | Sierra | Zendesk AI | Plain |
|---|---|---|---|---|---|---|---|---|
Works over ticketing with no named connector | Yes, via Flexible Ticketing System | No, Pylon is the queue | Custom work required | Supported platforms only | Custom work required | Possible via scoping | No, Zendesk is the queue | No, Plain is the queue |
Voice on the vendor's own telephony | Yes, separate from the ticketing layer | Not a voice platform | Yes | Via Intercom voice | Yes | Yes, a core strength | Yes, Zendesk Talk | Not a voice platform |
You keep your existing queue | Yes | Only if it is Pylon | Yes, where supported | Yes, where supported | Yes, where supported | Yes, scoped | Only if it is Zendesk | No |
Two gaps in our own row are worth reading carefully. Lorikeet's generic ticketing path is implementation-assisted rather than self-serve, so a team that wants to be live this afternoon on a named connector will find a ticketing-first vendor faster. And Lorikeet's inference runs in the United States even when customer data is stored in the European Union or Australia, which some buyers will need to raise with their compliance team. Teams in regulated categories can read more in our analysis of AI platforms for end-to-end resolution in regulated industries.
