Deflection is the easy half of customer support. A bot that answers "where are your office hours" looks impressive in a demo and falls apart the moment a ticket needs four tool calls, a third-party lookup, and a decision that survives an audit. This ranking is about the hard half: platforms that finish multi-step work, not platforms that end the conversation.
A multi-step resolution platform is an agentic AI system that completes a customer request that requires several actions in sequence - verify identity, query a system of record, take an action that changes state, confirm the outcome, and escalate cleanly if blocked - rather than answering a single question and closing the chat. In 2026, the gap between vendors is no longer who can deflect FAQs. It is who can resolve the ticket that touches three systems and one regulator.
Deflection counts a ticket as "handled" when the customer goes away. Resolution counts it as handled when the underlying problem is fixed. These are different metrics, and vendors quote whichever flatters them.
Multi-step tasks (lookup, risk check, state change, confirm, escalate-if-blocked) separate genuine agents from retrieval-and-reply bots wearing an agent label.
Gartner predicts 80% of common customer service issues will be resolved autonomously by 2029, up from low double-digits in 2024 - and "resolved" is the operative word.
Pricing tells you what a vendor optimizes for. Outcome-only pricing rewards finishing easy tickets; deflection pricing rewards ending conversations. Neither rewards the hard multi-step work.
State management and error recovery (what happens when the third tool call returns a 5xx mid-chain) is where most platforms quietly hand off to a human and call it a resolution.
Last updated: June 2026
The phrase "deflection rate" should make a buyer suspicious. It measures how many customers the bot got rid of, not how many problems it solved. For a simple business that distinction is academic. For a business where a request means "unlock my account," "recover my failed transfer," or "process my insurance claim," deflection is the wrong target entirely. Those tickets are multi-step by nature: the agent has to authenticate the person, read the relevant record, do something that changes the world, confirm it worked, and bail out safely if it cannot. This ranking judges the seven platforms below on that capability - chaining real actions across real systems and finishing the job - and is honest about where each one stops.
What Does "Multi-Step Resolution" Actually Mean?
Multi-step resolution is the ability of an AI agent to complete a customer request that requires an ordered sequence of dependent actions, holding state across each one and recovering when a step fails, rather than returning a single answer from a knowledge base. A mature platform resolves the request end to end and produces a record of what it did.
The category splits cleanly around what the agent can do when the request is not a question. First-generation bots retrieve an answer and reply. They deflect. Second-generation agents take an ordered set of actions: confirm who the customer is, query the system of record, change something (refund a fee, lock a card, update a claim), verify the change landed, and escalate with full context if a guardrail or an outage blocks them. The dividing line is state and recovery. A bot that can call one tool is not the same as an agent that can call five in the right order and still do the right thing when the fourth one errors.
Deflection: A support metric counting tickets the AI ended without a human, regardless of whether the customer's underlying problem was solved. High deflection can coexist with low resolution.
Multi-step action chain: An ordered sequence of dependent tool calls the agent executes to resolve one ticket (for example: authenticate, query balance, file dispute, update CRM, send confirmation), maintaining state and handling errors throughout.
Lorikeet is an AI customer support platform built for complex and regulated businesses - fintech, financial services, healthcare, insurance, and gaming - where most tickets are multi-step by nature. Its concierge resolves issues end to end across chat, email, voice, SMS, and WhatsApp, and its Team of Agents pattern dispatches sub-agents to call third parties, send email, and coordinate the steps a single reply cannot. Roughly 80% of its customers are US financial institutions and fintechs, the segment where deflection is least useful and resolution matters most.
At-a-Glance Comparison
At a glance
Platform: Lorikeet · Resolution model: End-to-end multi-step across chat, email, voice, SMS, WhatsApp; Team of Agents dispatches sub-agents · Best For: Complex and regulated businesses where tickets need several actions plus an audit trail · Pricing: ~$0.80–$0.95/chat-email-SMS resolution, ~$1.20–$1.50/voice, escalations not charged
Platform: Decagon · Resolution model: Multi-step agent across voice, chat, email with white-glove builds · Best For: Large enterprises with budget and engineering for a long deployment · Pricing: Custom; reported median near $400K/year
Platform: Sierra · Resolution model: Agentic resolution, outcome-only billing · Best For: Enterprises wanting to pay only on full resolution · Pricing: Custom; reported $50K-$200K/year plus per-outcome
Platform: Fin by Intercom · Resolution model: Outcome-priced agent on top of a helpdesk · Best For: Intercom customers wanting fast drop-in resolution · Pricing: $0.99 per resolution plus helpdesk seats
Platform: Gradient Labs · Resolution model: Agentic resolution focused on financial services · Best For: European fintechs and banks wanting a procedure-driven agent · Pricing: Custom (contact sales)
Platform: Salesforce Agentforce · Resolution model: Agents on the Salesforce platform, per-action billing · Best For: Salesforce-native orgs keeping support in-platform · Pricing: ~$2 per conversation/action (Flex Credits)
Platform: Ada · Resolution model: Resolution agent expanded from a chatbot lineage · Best For: Mid-market teams with high chat volume · Pricing: Custom; reported median near $70K/year
The 7 Best AI Platforms for Multi-Step Resolution in 2026
1. Lorikeet
Lorikeet is the AI customer support platform built for complex and regulated businesses, and it leads this list because multi-step resolution is the thing it was designed to do rather than a feature added on top of a deflection product. Its concierge resolves tickets end to end across chat, email, voice, SMS, and WhatsApp, and its Team of Agents pattern dispatches sub-agents to handle the parts of a task a single reply cannot - calling a merchant on a dispute, emailing a pharmacy, coordinating a handoff. The honest limitation: it is purpose-built for regulated, complicated support, so a simple high-volume FAQ deflection use case is more platform than that buyer needs.
Key Features
Team of Agents: dispatches sub-agents to call third parties, send email, and coordinate steps, so one ticket can fan out into the multiple actions a real resolution requires.
Natural-language and deterministic Structured Workflows, combinable in a single interaction - plain-English reasoning for ambiguity, deterministic paths where a step must happen the same way every time.
Defence in depth: pre-launch adversarial simulations and red-teaming, inbound message checks, outbound guardrails, and 100% post-facto QA - so the multi-step behavior is provable before go-live, not discovered in production.
Omnichannel on one engine: chat, email, voice (sub-1-second latency, multilingual), SMS, WhatsApp, plus outbound re-engagement, so a task that starts in chat can finish on a call without the agent losing state.
Coach: standalone analytics and 100% automated QA (root-cause analysis, ticket quality score, resolution verification) that audits whether the agent actually resolved the task, at roughly $0.25–$0.30 per ticket.
Ideal For
Fintech, financial services, healthcare, insurance, and gaming teams whose tickets are multi-step by default - identity verification, disputes, transfers, claims, account changes - and who need the agent to chain those steps, recover from a failed tool call, and leave an audit trail their compliance team can sign off on. Lorikeet reports regulated customers reaching high automation rates with equal-or-better CSAT, and it passed security review at major US banks.
Pricing
Per resolution: roughly $0.80–$0.95 per chat, email, or SMS resolution and roughly $1.20–$1.50 per voice resolution, with Coach around $0.25–$0.30 per ticket. The customer defines what counts as a resolution and escalations are not charged, which means Lorikeet is not paid for ending a conversation it did not finish. Against a human baseline of roughly $1.25-$4 per handled ticket, the per-resolution math favors automation on the tickets the agent genuinely closes.
2. Decagon
Decagon is a high-end enterprise AI agent platform that handles multi-step resolution across voice, chat, and email, with named consumer and fintech deployments processing large interaction volumes. It is a genuine agent, not a deflection bot, and it competes directly on the hard tickets. The trade-off shows up in deployment: vendors at this tier sell embedded engineering as a feature, and the honest read is that it is partly a tax you pay because the platform takes real work to configure.
Key Features
Multi-step agent across voice, chat, and email on one platform.
Per-conversation or per-resolution pricing, customer-selectable.
White-glove deployment with embedded engineering during launch.
Production deployments handling millions of interactions.
Strong enterprise procurement and support tooling.
Ideal For
Large enterprises with multi-million-dollar support budgets and engineering capacity to support a months-long build, who want a top-of-market agent and can absorb the cost of embedded delivery.
Pricing
No published rates. Industry data suggests a platform fee plus per-conversation or per-resolution fees, with median total contract value reported near $400,000 per year.
3. Sierra
Sierra is the enterprise AI agent company from Bret Taylor and Clay Bavor, which scaled to $100M ARR in 21 months and past $150M ARR by early 2026, per TechCrunch. It resolves multi-step tasks and bills purely on outcomes. The pitch is incentive alignment. The side effect worth naming: a vendor paid only on full resolution has a quiet reason to favor the tickets that resolve easily, which are not the multi-step ones a regulated business most needs handled.
Key Features
Outcome-only pricing: customers pay when the AI fully resolves a case; escalations cost nothing.
Multi-step resolution across voice, chat, and email.
Branded "AI Persona" deployment model.
High-touch implementation with embedded Sierra staff.
Strong enterprise procurement story.
Ideal For
Large enterprises that want billing tied strictly to successful resolutions and have the procurement appetite for a six-figure annual commitment.
Pricing
Not published. Enterprise contracts reportedly $50,000-$200,000 per year, with per-resolution rate negotiated case by case.
4. Fin by Intercom
Fin by Intercom is the AI agent layered on Intercom's messenger and helpdesk, priced at $0.99 per resolution - the lowest published outcome price in the category. Fin can chain actions and resolve genuine tasks, especially for teams already living in Intercom. The thing to understand about the price: $0.99 per resolution still rewards the vendor for closing the easy tickets, and the hard multi-step ones are where the real cost and the real risk sit.
Key Features
$0.99 per resolved outcome - among the lowest published per-resolution rates.
Action-taking and multi-step flows on top of the Intercom helpdesk.
Works with Salesforce and HubSpot helpdesks, not only Intercom.
Fast trial-to-deployment path.
Optional copilot for human agents.
Ideal For
High-volume teams already using Intercom that want the lowest published per-outcome price and a quick path to resolving common, moderately complex tickets.
Pricing
$0.99 per resolution, plus Intercom helpdesk seats for teams not already on the platform, plus optional copilot per user.
5. Gradient Labs
Gradient Labs is an agentic support platform focused on financial services, built to follow procedures and resolve tasks rather than deflect, with a customer base weighted toward European fintechs and banks. It is a credible multi-step resolution agent in regulated contexts, which puts it in direct conversation with Lorikeet. The honest read is that it is a newer and more narrowly scoped entrant, with a smaller channel footprint than the omnichannel platforms above.
Key Features
Agentic resolution that follows defined procedures for regulated workflows.
Financial-services focus with attention to compliance and controls.
Action-taking against systems of record, not retrieval-only replies.
Quality and review tooling oriented to regulated buyers.
European fintech and banking deployments.
Ideal For
European fintechs, banks, and financial-services teams that want a procedure-driven resolution agent built with regulatory controls in mind.
Pricing
Custom (contact sales). Rates are scoped per deployment and not published.
6. Salesforce Agentforce
Salesforce Agentforce brings autonomous agents to the Salesforce platform, resolving multi-step tasks against Salesforce data and connected systems and billing per action through Flex Credits. For organizations that run support inside Salesforce, keeping resolution in-platform is the obvious appeal. The trade-off is that the agent's strength tracks how deeply your data and processes already live in Salesforce, and per-action pricing can climb on genuinely multi-step tickets.
Key Features
Autonomous agents native to the Salesforce platform and data model.
Multi-step actions against Salesforce records and connected systems.
Per-action billing via Flex Credits (around $2 per conversation or action).
Tight integration with Service Cloud and the broader Salesforce stack.
Coexists with specialist platforms; Lorikeet, for instance, runs alongside Agentforce in some deployments.
Ideal For
Salesforce-native organizations that want their resolution agent to live in-platform and operate directly on Salesforce data.
Pricing
Consumption-based via Flex Credits, commonly cited at roughly $2 per conversation or action, layered on existing Salesforce licensing.
7. Ada
Ada is an established vendor that has expanded from chat into voice and email and now pitches an autonomous resolution agent. It handles breadth well and has a long enterprise track record. The architectural caveat is real: platforms that grew out of a chatbot lineage carry that origin into how they handle multi-step state and recovery, and depth on genuinely complex chains is harder to retrofit than breadth.
Key Features
Claimed autonomous resolution rate up to the low-to-mid 80s on supported workflows.
Multi-channel: chat, voice, and email.
Mature integrations with Salesforce, Zendesk, and major helpdesks.
Knowledge-base ingestion at scale.
Established enterprise deployment playbooks.
Ideal For
Mid-market and enterprise teams with high inbound chat volume that value a long track record and broad channel coverage over deep regulated-workflow specialization.
Pricing
Not published. Marketplace data shows median annual contracts reported near $70,000, with a wide range by company size.
The cost gap between deflecting a ticket and resolving it is the whole game: a human-handled ticket runs roughly $1.25-$4, while an AI resolution can run under a dollar - but only if the agent actually finishes the task. See how Lorikeet resolves multi-step tickets end to end.
How to Choose a Platform for Multi-Step Resolution
Most buying guides start with deflection rate, CSAT, and time to first response. For multi-step work those are downstream of one question: can the agent finish a task that takes several dependent actions, and prove it did. The five lenses below separate genuine resolution from dressed-up deflection.
Action Chaining and State
The agent has to call several tools in the right order without losing the thread - authenticate, query, act, confirm. Ask a vendor to show a single ticket where the agent ran four or more dependent actions and finished. If every demo is a one-tool lookup, you are looking at a deflection product with an agent label.
Error Recovery
Resolution is judged on the unhappy path. Ask what happens when the third system in a chain returns a 5xx or a timeout mid-task. The honest answers are retry, roll back, or escalate with full context. The answer that should worry you is silence, because that usually means the customer gets a half-finished action and no record of it.
Deterministic Plus Natural-Language Control
Some steps must happen the same way every time (a disclosure, a dollar-threshold block); others need judgment. The strongest platforms let you combine deterministic workflows with natural-language reasoning in one interaction. A platform that is all free-form reasoning is hard to make repeatable; one that is all decision trees is brittle on ambiguity.
Pre-Launch Validation
Multi-step behavior is where agents go wrong in ways you cannot see until production - unless you can test it first. Ask whether you can run adversarial simulations and a guardrail test suite before go-live and read the results. Validating behavior in advance is the difference between approving evidence and approving faith.
Same-Agent Omnichannel
A multi-step task often crosses channels: it starts in chat and finishes on a call. If voice runs on a different stack than chat, the customer repeats themselves and the agent loses state. Look for one agent across chat, email, voice, and SMS on a single engine, not separate bots stitched together with a transcript handoff.
Questions to ask your vendor
Demos are built to look good. These questions are built to make a demo break.
Show me one ticket where the agent ran four or more dependent actions in order and resolved it end to end.
What happens when the third tool call in a chain returns a 5xx - retry, roll back, or escalate, and what does the customer see?
Can I run adversarial simulations and a guardrail test suite before go-live and read the pass/fail report?
How do you define a resolution for billing, and who decides whether a ticket counts as resolved?
Does voice run on the same workflow engine as chat, and can the agent take actions on a call rather than route to a human?
Show me how the agent leaves a record of every action it took on a multi-step ticket.
Lorikeet's Take on Resolution vs. Deflection
Deflection is a metric that flatters the vendor and tells the buyer almost nothing. A bot can deflect 80% of tickets by being good at ending conversations, and a business in fintech, healthcare, or insurance will discover that the 20% it could not finish are the only ones that ever mattered. The right question is not how many customers went away. It is how many problems got solved, and whether the agent can prove what it did on each one.
Lorikeet is built around that question. The concierge resolves multi-step tasks end to end, the Team of Agents pattern fans a single ticket out into the actions a real resolution needs, deterministic and natural-language workflows combine in one interaction, and defence in depth - simulations, message checks, guardrails, and 100% QA - makes the behavior provable before launch. Pricing follows the same logic: the customer defines what counts as a resolution and escalations are not charged, so the platform is not paid for a conversation it did not finish. If your tickets are multi-step by nature, see how Lorikeet handles end-to-end resolution.
Key Takeaways
Deflection and resolution are different metrics; vendors quote whichever flatters them, and only resolution reflects whether the customer's problem was actually solved.
Multi-step resolution is defined by action chaining, state management, and error recovery on the unhappy path - not by how many FAQs a bot can deflect.
Pricing reveals incentives: outcome-only billing favors easy tickets, deflection pricing favors ending conversations, and neither directly rewards finishing hard multi-step work. Lorikeet lets the customer define a resolution and does not charge for escalations.
Gartner predicts 80% autonomous resolution of common issues by 2029 - but in regulated businesses the bar is finishing the multi-step tickets, not deflecting the simple ones.
Lorikeet, Decagon, Sierra, and Gradient Labs are genuine multi-step resolution agents; Lorikeet leads for complex and regulated workflows where the task must be finished, recovered when blocked, and provable before go-live.
Conclusion
The 2026 question is no longer whether to deploy an AI agent. It is whether the agent resolves the tickets that take several steps, or merely deflects the ones that take one. The seven platforms above all claim resolution; the ones that earn the word can chain dependent actions, recover when a step fails, work the same across channels, and leave a record of what they did.
Lorikeet is the answer for complex and regulated businesses whose tickets are multi-step by default and whose toughest internal stakeholder is the person who has to sign off on what the agent is allowed to do. Decagon and Sierra are strong enterprise agents, Gradient Labs is a credible regulated-finance specialist, Fin and Agentforce resolve well inside their ecosystems, and Ada brings breadth and track record. Match the platform to whether your hard tickets are the ones that matter.
If your tickets need several steps and an audit trail, book a Lorikeet demo and bring your hardest multi-step tickets - we will run them against your guardrails before you sign.
