Decagon is live · custom pricing · last verified 5 Nov 2026 · viability monitor
Decagon
Decagon builds AI concierge support agents for chat, voice, and email using natural-language Agent Operating Procedures.
Pick Decagon when you run a high-volume enterprise contact center and need agents live in weeks. AOPs let CX leads rewrite behavior in plain English, and the Simulations, Experiments, traceability, and Watchtower stack is what gets a self-improving agent past risk and compliance. The catch: it assumes an internal owner iterating weekly, and Autopilot's self-improvement needs your own governance.
Verdict generatedVerified 9d ago · liveness 69/100 · cite: rightaichoice.com/tools/decagon
- Large enterprises in retail, travel, financial services, healthcare, technology, media, telecom, or insurance
- CX teams that want to change agent behavior in plain English instead of waiting on engineering sprints
- Organizations unifying chat, voice, and email with shared memory and cross-channel context
- Contact centers chasing higher deflection and lower cost per resolution while holding CSAT
- Companies determined to build and own their agent stack on raw model APIs
- Organizations without an internal owner to iterate on agent workflows week over week
- Buyers running low support volume where deployment overhead never pays back
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Skip Decagon if you have no internal owner who can iterate on agent workflows weekly, or if you're set on building and owning the agent stack yourself on raw model APIs.
Decagon sells through enterprise contracts, so the real cost is the contract plus the internal owner you staff to iterate on AOPs weekly. That fits contact centers with thousands of monthly conversations where a point of deflection pays for itself; below that volume the deployment overhead dominates. Smaller teams comparing against self-serve chat-agent tools should model the dedicated owner as part of the price.
In short
Decagon — Decagon builds AI concierge support agents for chat, voice, and email using natural-language Agent Operating Procedures. Best for Large enterprises in retail, travel, financial services, healthcare, technology, media, telecom, or insurance, CX teams that want to change agent behavior in plain English instead of waiting on engineering sprints, Organizations unifying chat, voice, and email with shared memory and cross-channel context. Pricing is on request (custom quotes).
What is Decagon?
Decagon — Decagon builds AI concierge support agents for chat, voice, and email using natural-language Agent Operating Procedures. Best for Large enterprises in retail, travel, financial services, healthcare, technology, media, telecom, or insurance, CX teams that want to change agent behavior in plain English instead of waiting on engineering sprints, Organizations unifying chat, voice, and email with shared memory and cross-channel context. Pricing is on request (custom quotes).
Decagon is an enterprise AI concierge platform for customer support. Its core idea is the Agent Operating Procedure (AOP): agent workflows defined in natural language, so a CX lead can rewrite how an agent behaves without an engineering sprint or a vendor ticket. Chat, voice, and email sit on one intelligence layer with shared memory, so a conversation that starts in chat and continues on voice keeps its context. Duet, the platform's AI partner, reads real conversations to find workflow gaps and generates, tests, and refines AOPs. Around it sits an unusually deep QA stack: Simulations at scale, unit testing, versioning, live A/B Experiments, step-by-step traceability of model calls and knowledge articles, Watchtower always-on QA, and Voice of Customer reporting. October 2026 brought Voice 3, running on Chord, Decagon's in-house speech model, alongside detection of personal agents and a dedicated channel to serve them under PACT, a brand-controlled consent and trust protocol. The target buyer is the large enterprise contact center across retail, travel and hospitality, financial services, health and wellness, technology, telecom, media, and insurance. Decagon Assist feeds human reps AI context mid-conversation, and the vendor publishes customer results including 70% chat and voice resolution and 80% deflection. 2026 also saw expansion into Benelux and Brazil. Against chat-only point solutions, Decagon wins on omnichannel depth and shared memory. Against building on raw model APIs, it trades some control for considerably faster time to value.
Key features
- Agent Operating Procedures (AOPs) define agent workflows in natural language
- Duet analyzes real conversations and generates, tests, and refines AOPs
- Duet Autopilot runs self-improving agents with production governance controls
- Voice 3 voice channel powered by Chord, Decagon's in-house speech model
- Chat agents for on-brand, safe replies across complex workflows
- Email agents for always-on contextual resolution
- Single intelligence layer unifying chat, voice, and email
- Cross-channel and cross-session memory preserving customer context
- Personal agent detection and a dedicated channel to serve them
- PACT consent and trust protocol with brand-controlled permissions
- Decagon Assist gives human reps AI-backed context during live conversations
- Simulations at scale and unit testing before workflow changes ship
- Versioning and Experiments for live A/B testing of agent updates
- Step-by-step traceability of model calls, workflow triggers, and knowledge articles
- Watchtower always-on QA monitoring against custom criteria
Behind the verdict
The reason to shortlist Decagon is the operating model, not any single feature. AOPs move agent behavior out of configuration languages and into plain English, which means the CX lead who knows the refund policy can change the refund policy. That sounds small until you compare timelines: the alternative is a ticket to engineering and a two-week wait for a workflow tweak. We'd reach for this when support volume is high enough that deflection moves the P&L and you have someone who owns AI operations week to week. Enterprises in retail, travel, financial services, or telecom with a real QA function will get the most out of Simulations, live Experiments, and the step-by-step traceability that auditors ask for. Decagon Assist is a quiet bonus for the hybrid case where human reps still take escalations. Pass if your stack strategy is to build on raw model APIs in-house. You'll get more control and no vendor lock, but you'll also rebuild testing, versioning, and QA tooling from scratch. Pass too if volume is low, because deployment overhead won't pay back, or if nobody internally can own the iteration loop. Two caveats worth stating plainly. Self-improving agents under Duet Autopilot improve only as fast as your governance lets them, so bring your own policy for what ships automatically. And the newer personal-agent channel under PACT is early: the consent model is thoughtful, but expect to test it against your own compliance review rather than take it on faith. The closest alternative depends on where you sit. If chat is your only channel, chat-only point solutions cost less and deploy faster. If voice quality is the bottleneck, Voice 3 with Chord is the current answer here, but it's worth a side-by-side bake-off against your own call recordings before committing.
Models under the hood
as of 2026-11-08
Limitations
- Decagon is an enterprise concierge platform for customer support, and the platform assumes operational maturity: someone has to own AOP iteration week over week, and self-improving Duet Autopilot agents inherit whatever governance and review process you bring — Decagon ships the controls, not the process.
- Announced performance figures (80% deflection, 65% support-cost decrease, 70% chat and voice resolution) are vendor-reported results drawn from customer stories, not independently verified.
- Voice 3 and Chord, Decagon's in-house speech model, launched in October 2026, so independent evidence on voice quality is still accumulating.
- The same October release introduced personal-agent detection and the PACT consent protocol — new enough that best practices for serving third-party agents are still forming.
as of 2026-10-22
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Is Decagon still active?
Decagon is live · custom pricing · last verified 5 Nov 2026 · viability monitor.
Viability score
How well maintained and how widely used is Decagon? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: November 2026
How we score →What's new in Decagon
Checked 11 days agoAcross the latest 5 updates: 3 launches and 2 news mentions.
Introducing the Personal Agent Consent & Trust Protocol (PACT)
Decagon published PACT, a consent and trust protocol for serving personal agents, with permissions controlled by the brand.
Introducing Voice 3 and Chord, our new speech model
Decagon released Voice 3 and Chord, a new in-house speech model for its voice channel.
Personal agents are here. Meet them on your terms.
Decagon launched detection of personal agents plus a dedicated channel to serve them, with permissions you control.
Growing in Benelux
Decagon expanded its operations into the Benelux region.
Decagon expands into Brazil
Decagon expanded into Brazil, extending its support platform to a new market.
What do users say about Decagon?
What people actually say about Decagon — is it worth it?
We scanned public community sources for Decagon on Oct 10, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 0 of the posts we fetched could be positively tied to Decagon. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
How we verified this
Every listed fact about Decagon — pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 5 Nov 2026. Analysis is drafted with AI models and checked against our editorial standards before publishing; see how we verify.
Verification history
We have re-verified Decagon 24 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing page
- — changed our verdict, summary, description, features, who it suits
- — changed platforms, limitations, pricing page, our verdict, features, summary, who should skip it, description, who it suits
- — changed pricing page
- — changed our verdict, description, features, hidden costs, limitations, switching in, switching out, who should skip it, pricing fit, setup time, summary, use cases
- — changed limitations
Showing the 6 most recent of 24 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Decagon cost in 2026?
Decagon: Pricing is on request (custom quotes). Pricing last verified 5 Nov 2026.
Where the pricing makes sense
The company stage and team size where Decagon's pricing actually pencils out — and where peers do it cheaper.
Decagon sells through enterprise contracts, so the real cost is the contract plus the internal owner you staff to iterate on AOPs weekly. That fits contact centers with thousands of monthly conversations where a point of deflection pays for itself; below that volume the deployment overhead dominates. Smaller teams comparing against self-serve chat-agent tools should model the dedicated owner as part of the price.
Has Decagon's pricing changed?
No plan price for Decagon has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Decagon?
Pick Decagon if you are Large enterprises in retail, travel, financial services, healthcare, technology, media, telecom, or insurance, CX teams that want to change agent behavior in plain English instead of waiting on engineering sprints, Organizations unifying chat, voice, and email with shared memory and cross-channel context, Contact centers chasing higher deflection and lower cost per resolution while holding CSAT.
Look elsewhere if you are Companies determined to build and own their agent stack on raw model APIs, Organizations without an internal owner to iterate on agent workflows week over week, Buyers running low support volume where deployment overhead never pays back, Teams needing deep custom integration work beyond documented pre-built connectors.
Skip it if Skip Decagon if you have no internal owner who can iterate on agent workflows weekly, or if you're set on building and owning the agent stack yourself on raw model APIs.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Decagon actually fits — and what changes day-one when you adopt it.
You inherit a backlog of refund and order-status intents handled by humans. You write AOPs in plain English for each, let Duet generate drafts from real transcripts, and run Simulations against past conversations before enabling them in chat.
Outcome: Tier-1 intents resolve in the agent, and each behavior change ships without an engineering sprint.
A new agent version underperforms on a specific intent. You compare it against the live version with Experiments, then use step-by-step traceability to see which workflow trigger or knowledge article drove the wrong decision.
Outcome: You isolate the regression, fix the AOP, and roll back or promote with measured impact.
Phone volume spikes seasonally. You extend the same agent to Voice 3 with Chord, keeping cross-channel memory so callers who started in chat aren't asked to repeat themselves, and watch Watchtower for QA drift.
Outcome: Voice deflection above 50% without a separate voice vendor or a rebuilt knowledge base.
Use cases
- Deflect 60–80% of tier-1 tickets across chat, email, and voice with one agent
- Automate order status, refund, and subscription changes end to end
- Handle high-volume phone inquiries in brand voice with Decagon Voice 3
- Personalize repeat interactions with cross-channel memory to lift CSAT
- Run weekly simulation suites to catch regressions before production
- Give human reps real-time AI context with Decagon Assist
- Monitor 24/7 conversation quality with Watchtower against custom criteria
- Serve customers' own personal agents through a governed channel under PACT
Setup time & first value
How long it actually takes to get something useful out of Decagon — broken out by persona, not the marketing-page minute.
Expect weeks rather than quarters to first production traffic: AOP authoring and Duet-generated drafts are fast, but Simulations, guardrail configuration for identity verification and refunds, and connection to your ticketing and knowledge systems are the real timeline. Voice 3 adds telephony and brand-voice tuning on top.
Switching to or from Decagon
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a legacy chat vendor: rebuild intents as natural-language AOPs, then validate them in Simulations against historical transcripts before cutover.
- →From a build-it-yourself model-API stack: port prompts and tool calls into AOPs and keep traceability and versioning in Decagon rather than your own harness.
- →From a scripted IVR: model phone flows as AOPs on Voice 3 and preserve context by routing chat history through shared memory.
- ↗To a build-your-own stack: export conversation data and workflow logic, then re-implement AOPs as prompts and tool calls in your own harness.
- ↗To a single-channel chat vendor: keep chat AOPs but expect to rebuild the cross-channel memory and voice layer you relied on.
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Decagon”, and we withheld 6: 6 could not be judged, because “Decagon” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Decagon.
Official links
What are the best Decagon alternatives?
The closest alternatives to Decagon we list are Giga, TalkStack AI and Spitch. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Giga | Contact Sales | Custom pricing | — |
| TalkStack AI | Paid | Pricing not published | — |
| Spitch | Contact Sales | Custom pricing | — |
Alternatives to Decagon
View all
Giga
Giga builds AI voice, chat and email support agents tuned to your resolution rate, escalation rate or CSAT — not a fixed script.
TalkStack AI
No-code AI voice and text agents that handle support, sales, and scheduling in 20+ languages.
Tools that pair well with Decagon
Common stack mates teams adopt alongside Decagon, with the specific reason each pairing earns its keep.
Giga
Giga builds AI voice, chat and email support agents tuned to your resolution rate, escalation rate or CSAT — not a fixed script.
TalkStack AI
No-code AI voice and text agents that handle support, sales, and scheduling in 20+ languages.
Spitch
Modular LLM agentic AI platform that automates enterprise contact centers across voice and text in Swiss-German, German, French and Italian.
Decagon vs its closest rival
Decagon's closest head-to-head is with Cresta.
Our Decagon vs Cresta verdict written May 12, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Frequently asked questions
Best-of guides
For makers of Decagon
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