Move your team from Decagon to Sierra.
Each person gets a short interview, then a personalized step-by-step guide showing exactly where their saved work, prompts, and projects land in Sierra — and the few things that need a manual step, walked through. You send one invite; nobody becomes the help desk, and nothing in Decagon is changed or cancelled. Want to see where you’re paying for both first? Run the free audit.
Both platforms provide always-on monitoring of live agent conversations to catch quality issues, flag problematic interactions, and enable pre-deployment regression testing. On Decagon, QA is handled by Watchtower (real-time monitoring and flagging), QA Hub (collaborative human review of flagged conversations), Simulations (synthetic load testing of agent changes), and Trace View (step-by-step agent reasoning). On Sierra, quality monitoring lives in the Insights module: Monitors auto-flag conversations matching configurable criteria, and Experiments runs A/B tests of agent changes before wide rollout; individual conversation drill-downs show tool calls, knowledge lookups, and latency per turn. To migrate from Decagon to Sierra: document all Watchtower flagging criteria and recreate them as Sierra Monitors; replace Decagon Simulations with Sierra Experiments to pre-test agent changes; use Sierra's observability drill-downs in place of Trace View for individual conversation debugging. To migrate from Sierra to Decagon: map Sierra Monitor conditions to Decagon Watchtower rules; introduce QA Hub for collaborative review if your team currently does this asynchronously outside Sierra. In both directions, re-assign human reviewers to the destination platform's queue before cutting over live traffic.
- Warning: Decagon runs thousands of synthetic simulation conversations pre-deployment; Sierra's Experiments feature is an A/B test on live traffic rather than a fully synthetic pre-launch sandbox — teams with strict pre-production testing requirements should validate whether Sierra's approach meets their change-management policy.
- Warning: Decagon's Agent Workbench supports autonomous debugging of failed interactions; Sierra does not publicly advertise an equivalent autonomous debugging loop, meaning manual review via observability drill-downs is the primary path on Sierra.
Both products offer natural-language querying over conversation history, automated issue flagging, and insight dashboards for support leaders. On Decagon, the Insights and Reporting module provides voice-of-the-customer dashboards, 'Ask AI' for natural-language queries, and Root Cause Analysis for contact volume spikes. On Sierra, the Insights module in Agent Studio offers Explorer (ChatGPT-style queries over conversations), Monitors (automated conversation flagging), and Experiments (A/B testing of agent changes). To migrate from Decagon to Sierra: export or document any saved 'Ask AI' query templates and recreate them as Sierra Explorer queries; configure Sierra Monitors to replicate any Decagon flagging rules. To migrate from Sierra to Decagon: map Sierra Explorer query patterns to Decagon's Ask AI interface; note that Decagon adds AI-powered knowledge-gap Suggestions (surfacing unresolved queries) that Sierra does not explicitly list — verify if this matters for your QA workflow. In both directions, re-connect dashboards to your ticketing and BI tools after migration.
- Warning: Decagon includes Root Cause Analysis that automatically identifies drivers of contact-volume spikes; Sierra's equivalent (Explorer) is query-driven rather than automated — teams relying on automatic spike detection must create manual Monitors in Sierra.
- Warning: Sierra Experiments supports multivariate A/B testing of agent behavior changes; Decagon's A/B testing is scoped to AOP procedure sets rather than general agent experiments — assess whether Sierra's broader experimentation scope matches or exceeds your needs.
Both platforms enable agents to deliver personalized responses by drawing on stored customer context, though the mechanism differs: Decagon uses a first-party User Memory store built into the agent runtime, while Sierra achieves personalization through real-time lookups against integrated back-end systems (CRMs, order management, data warehouses). On Decagon, User Memory automatically captures and injects relevant facts (preferences, past issues, account context) at the start of each new conversation across all channels. On Sierra, personalization is driven by Agent Studio integrations that call CRM and order-management systems live during conversations; pre-built connectors cover Snowflake, Databricks, Google Cloud, AWS, and Redis, with custom connectors available via the Agent SDK. To migrate from Decagon to Sierra: audit what facts Decagon's User Memory holds for each customer, map these fields to equivalent CRM or data-warehouse fields, ensure Sierra integrations are configured to fetch them in real time, and retire the Decagon memory store only after confirming Sierra lookups surface the same context. To migrate from Sierra to Decagon: enable Decagon's User Memory feature and define a seeding process — either a one-time import of customer context from your CRM or a progressive fill as customers contact support.
- Warning: Decagon's User Memory is a first-party persistent store; Sierra has no equivalent standalone memory — if Sierra's CRM integration goes offline or returns no data, there is no fallback context store, making integration reliability critical.
- Warning: Decagon User Memory enables Guided Discovery (proactive product recommendations based on stored context); Sierra's personalization is reactive (fetch on demand) rather than proactively surfacing recommendations — teams that rely on guided upsell flows must rebuild this logic using Sierra's action framework.
Both platforms let non-technical admins define and update agent behavior in plain English without engineering involvement. On Decagon, support teams write Agent Operating Procedures (AOPs) in the Decagon console; on Sierra, teams use Ghostwriter inside Agent Studio, uploading SOPs, policy docs, or past transcripts to generate a deployable agent. To migrate from Decagon to Sierra: export your existing AOPs as text documents, then feed them into Sierra's Ghostwriter as knowledge sources. Re-configure escalation rules, response tone, and guardrails in Agent Studio's settings panel. To migrate from Sierra to Decagon: copy your Ghostwriter-generated agent descriptions and uploaded SOP docs into Decagon's AOP editor as natural-language instructions; adjust per Decagon's AOP versioning workflow so teams can roll back or A/B test procedure sets. In both directions, verify that escalation thresholds and tone settings are re-entered on the destination platform, since these are not in a portable export format.
- Warning: Decagon AOPs use a versioning and A/B-testing model built into the console; Sierra versions agent configs through Agent Studio's audit log but does not explicitly advertise A/B testing of procedure sets — validate this feature parity before switching if you rely on it.
- Warning: Sierra's Ghostwriter can ingest audio recordings as a knowledge source; Decagon's AOP editor is text-only, so any audio-based SOPs must be transcribed before import.
Both platforms deploy a single AI agent across chat, email, and voice from one control panel, eliminating per-channel vendor contracts. On Decagon, agents are configured once in the Decagon console and pushed to web chat (widget or embedded), email inbox, and voice simultaneously. On Sierra, a single agent configured in Agent Studio is published to chat, email, SMS, WhatsApp, phone (via the Voice add-on), and even ChatGPT across 58 languages. To migrate from Decagon to Sierra: re-build the unified agent in Agent Studio, then enable each channel through Sierra's deployment settings — note that voice requires Sierra's dedicated Voice add-on. To migrate from Sierra to Decagon: map each active Sierra channel to the equivalent Decagon channel; Decagon supports chat, email, and voice but does not list SMS or WhatsApp, so customers using those channels must plan an alternative or stay on Sierra for those touchpoints. In both directions, test that escalation paths and fallback behaviors carry over correctly per channel.
- Warning: Sierra supports SMS, WhatsApp, and ChatGPT as deployment channels; these are not listed as Decagon channels, so teams relying on those must find an alternative or keep them on a separate tool.
- Warning: Sierra's 58-language support is built into the runtime; Decagon does not publicly list a comparable language count — multilingual teams should verify coverage before switching.
- Warning: Sierra's Voice channel is a separate paid add-on; budget for this line item when costing a migration from Decagon (which bundles voice in its agent).
Both platforms position enterprise-grade security and compliance as a first-class feature, with no-training-data policies and access controls. Decagon enforces zero-day data retention with AI providers (OpenAI and Anthropic), auto-redacts PII via Google DLP before it appears in logs, controls access via RBAC and SSO (Okta/Microsoft Entra), and publishes a trust portal at trust.decagon.ai. Sierra holds SOC 2, ISO 27001, ISO 42001, HIPAA, GDPR, FedRAMP, and PCI DSS certifications and explicitly states customer data is never used to train models. To migrate from Decagon to Sierra: request Sierra's compliance documentation package and compare it against your vendor assessment checklist; pay particular attention to PII-handling (Decagon names Google DLP as the redaction engine — Sierra's mechanism is not publicly detailed). To migrate from Sierra to Decagon: verify that Decagon's certifications satisfy your requirements; FedRAMP is present on Sierra but not listed for Decagon, which is a blocker for US federal customers. SSO configuration must be re-provisioned on the destination platform in both directions.
- Warning: Sierra lists FedRAMP certification; Decagon does not publicly claim FedRAMP — US federal or regulated-government customers cannot simply swap to Decagon without confirming this gap.
- Warning: Decagon explicitly names Google DLP as its PII-redaction engine; Sierra does not publicly detail its PII-redaction mechanism, so customers with specific PII-handling audit requirements must obtain this information from Sierra's security team before migrating.
- Warning: Decagon publishes a public trust portal (trust.decagon.ai) with audit logs; Sierra's equivalent documentation path is not publicly listed — ask your Sierra rep for the equivalent artifact.