Lorikeet vs Sierra (2026)

Connectors cover ticketing and messaging (Zendesk, Zendesk Messaging, Front, Help Scout, Slack), CRM (Salesforce, including running workflows inside it) and revenue tooling. Tools are set up as APIs or webhooks, so the agent reads data and takes actions in any internal system, scoped least-privilege with short-lived single-operation tokens and HMAC-signed inbound webhooks.

Sourcelorikeetcx.ai/integrationsread 2026-09-05

Agents reach the customer's systems of record to carry out end to end transactions, including card and ACH payments, returns, authentication and cancellation saves, and those system interactions are held deterministic and bound by policy. Agent Studio offers ready integrations and an integration framework for proprietary systems, and the Agent SDK builds the rest, so the range of connected systems depends on what each team sets up. Integrations also route a conversation to the customer's care team with its full context.

Sourcesierra.ai/product/agent-sdkread 2026-09-05

The agent runs multi-step work by following the customer's own standard operating procedures without improvising, whether a three-step form or a forty-step refund flow. Workflow steps include workflow matching, ticket triage, gather, get status, choice and route based on problem, with tool calls such as retrieving recent transactions shown inline. Lorikeet describes specialized agents working together in real time across channels to deliver end-to-end resolutions, and the login surface is a workflows console.

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Agents are put together from skills such as triage, respond and confirm, combined into multistep workflows for a specific use case. In Agent Studio a team defines those journeys step by step or has AI generate them from its existing operating procedures, and Ghostwriter builds or changes an agent from a description of how it should behave. Flexibility is set per workflow, so a team can move between deterministic execution and open ended reasoning on the same agent.

Sourcesierra.ai/product/agent-sdkread 2026-09-05

Five channels run on one engine, phone, SMS, chat, email and WhatsApp, with an SDK as a further delivery surface, and specialized agents work together in real time across chat, email, voice and SMS to deliver end-to-end resolutions. Voice runs at sub-second latency on the same engine, so a customer who starts in chat does not repeat themselves on a call, and every channel inherits the same guardrails and logging.

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An agent is built once and deployed across chat, phone, email, SMS and messaging, with the same skills and policies applying on each. A single agent working across every channel is the core idea of the platform.

Sourcesierra.ai/product/agent-sdkread 2026-09-05

The agent is grounded in the customer's own documented procedures, not retrieval alone. Lorikeet follows the customer's SOPs, taps into its systems and handles its highest-stakes workflows, and says its architecture goes beyond retrieval-augmented generation by following the procedures themselves, so the agent neither just retrieves nor invents process. Help centers, reference materials and internal tools supply the surrounding material, and the customer writes and owns the procedures.

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Agents ground answers in the customer's own content through retrieval built into Agent OS, and the Agent Data Platform joins that knowledge with customer context from other systems. In Agent Studio a team connects its knowledge sources and views and edits help center content, FAQs and policies. Sierra spots common themes missing from the knowledge base and drafts new articles from how care representatives resolved edge cases, so the store grows without manual updates.

Sourcesierra.ai/product/agent-sdkread 2026-09-05

Customer memory keeps context across every touchpoint, so a customer who starts in chat does not repeat themselves on a call, because the channels run on one engine and persistence spans channels. No scope, lifetime, retention window or deletion control for that memory is described.

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Agents personalize each conversation from agent memory and the customer's profile, and the Agent Data Platform brings together what the agent knows about a specific customer across sessions, channels and systems, so context carries from one conversation to the next. How long that memory lasts, and whether a customer's record of it can be viewed or cleared, is not public.

Sourcesierra.ai/product/insightsread 2026-09-05

Runtime guardrails detect sensitive topics and escalate to the customer's team before the agent proceeds, and outbound checks block the agent from disclosing withheld fields or acting outside policy, independently of the model. Tools are scoped least-privilege, dangerous actions need per-user elevation, and the integration is red-teamed against prompt injection and scope escalation before go-live. People write the procedures the agent follows, and handoffs arrive with the investigation complete.

Sourcelorikeetcx.ai/product/trustread 2026-09-05

Sierra wraps its models in supervisory layers that check outputs against scope and policy before the agent responds, to reduce hallucination and prevent abuse. Access to systems of record is deterministic and controlled, and the customer's policies and security procedures are enforced outside the model. Flexibility settings on each workflow limit how far the agent may improvise.

Sourcesierra.ai/product/trust-and-reliabilityread 2026-09-05

Lorikeet holds SOC 2 Type II, ISO 27001:2022, HIPAA and GDPR, all independently verified and published on a public Vanta trust center at trust.lorikeetcx.ai with reports downloadable under NDA and refreshed annually, and it signs BAAs for healthcare customers. Controls include automatic PII redaction, minimum-necessary PHI handling, TLS 1.3 in transit and AES-256 at rest, no public internet access to production, row-level-security tenant isolation, SSO with hardware-key MFA, and third-party penetration testing.

Sourcelorikeetcx.ai/product/trustread 2026-09-05

Sierra holds ISO/IEC 27001:2022 and ISO/IEC 42001:2023 certificates, a SOC 2 Type II report, a HIPAA attestation for 2026 and PCI DSS Level 1 service provider certification. Its trust center also covers the European and California privacy laws, the CSA STAR registry and FedRAMP High, with controls and policies shared on request.

Personal information shared with an agent is encrypted and masked automatically, filters block topics and keywords a company puts off limits, and cardholder data runs on dedicated PCI infrastructure that never touches Sierra's core platform, models or storage. Customer data is never used to train models or shared with other customers. Who inside a customer's own organization can build, edit or publish an agent, through single sign on, roles or permissions, is not public.

Sourcesierra.ai/product/trust-and-reliabilityread 2026-09-05

Every customer interaction, model choice and action is tracked and fully explainable, not a black box. Every tool call, elevation request and reasoning step is logged with timestamps and can be replayed for a regulator examination, and Coach scores the quality of every conversation, human or AI.

Sourcelorikeetcx.ai/product/trustread 2026-09-05

Every agent action can be traced to its reasoning, with the knowledge sources and systems it used, and developers inspect API calls and logic traces while Agent Studio shows a live trace of each decision, tool call and response during a build.

Insights reports on CSAT, resolution and other metrics with custom reports, tags and categorizes conversations automatically, flags the ones that need a person's attention and sends alerts on abuse attempts or performance drops to a team's existing tools.

Explorer answers questions about thousands of conversations with themes, data and links to the conversations behind each finding, and delivers a weekly briefing on its own.

Sourcesierra.ai/product/insightsread 2026-09-05

Production runs on Google Cloud Platform in a private VPC with no public internet access and row-level-security tenant isolation, with US primary hosting and data storage residency available in Australia or the EU. Lorikeet's marketing articles name the US, Australia and the UK, while its trust page lists Australia and the EU.

Sourcelorikeetcx.ai/product/trustread 2026-09-05

Sierra hosts the platform as SaaS. Each subprocessor carries its own processing location on the trust center, and customers do not pick a region. There is no VPC, single tenant or on premises option. The FedRAMP High commitment points to separate government infrastructure, though no choice of residency regions is offered.

Sourcesierra.ai/product/trust-and-reliabilityread 2026-09-05

Each customer's configuration is written from its own standard operating procedures, by design. Lorikeet's test is that if you can explain to a human agent how to resolve a ticket, the agent can handle it, so time to value depends on the customer's procedure documentation. There is no template library, prebuilt agent catalog or marketplace, and the specialized agents in the Team of Agents architecture work together internally and are not assets a customer browses and adopts. Lorikeet also runs a separate site at lorikeet.tools, branded Toolshed, whose contents are not described.

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The Agent SDK ships with a range of AI skills and systems integrations to help customers go live faster, and skills such as triage, respond and confirm work as reusable building blocks. Customers assemble these pieces themselves. There are no named prebuilt agents, templates, library or marketplace.

Sourcesierra.ai/product/agent-sdkread 2026-09-05

OpenAI, Anthropic and Baseten are among the core subprocessors, so several model providers are in play, all under zero-data-retention agreements with no fine-tuning on customer data, and Lorikeet routes between providers by task. The routing is Lorikeet's own and model choice is tracked, not made by the customer, with no customer-facing selection or bring-your-own-model control.

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Agents draw on more than fifteen frontier, open weight and proprietary models chosen per job, and Sierra switches between LLM providers automatically to optimize performance and keep running during a provider outage. Model serving runs through subprocessors that include Anthropic, Google, AWS, Baseten, Fireworks AI, Together AI and Modal. Routing happens inside Sierra, and neither customers nor admins choose the model.

Sourcesierra.ai/blog/constellation-of-modelsread 2026-09-05

A developer documentation site at docs.lorikeetcx.ai covers a quickstart and API reference, and integrations use scoped Bearer-credential access and HMAC-signed inbound webhooks that the customer's systems call. Lorikeet also publishes public HTTP endpoints that outside AI assistants can call to query its support agent and book demos. The documentation sits behind a customer access code, so the platform API and its scope are open only to customers, and no SDK is advertised.

Sourcedocs.lorikeetcx.ai/guides/quickstartread 2026-09-05

Developers build, customize and deploy agents with the Agent SDK, which comes with documentation, interactive training and a development pipeline. Agent OS adds ready made tooling for building, deploying and managing agents. There is no public API reference or MCP server for the platform itself, so developers work through the SDK.

Sourcesierra.ai/product/agent-sdkread 2026-09-05

Testing covers both sides of go-live. Before launch, a pre-go-live simulation suite lets a security or compliance team validate agent behavior before unsupervised resolution begins. After launch, Coach reviews every ticket, not a sample, whether handled by people, AI or both, shows why performance is trending up or down, proposes fixes and, with approval, implements them, with automated quality assurance, thematic analysis, segmentation and export. Coach is sold to Lorikeet customers and standalone, is metered per ticket and is generally available.

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Agent Studio simulates conversations and scores the agent against defined outcomes, and a team builds a regression test suite that runs before each new version of the agent is released.

Voice agents are tested for transcription, background noise and speaker variation before going live, and the Agent SDK checks behavior across a wide range of scenarios in a development pipeline, so definitions are not edited live.

After launch, Insights runs experiments on variations of agent behavior such as handoff rules or levels of determinism, and Explorer compares performance before and after a change Ghostwriter has made.

Sourcesierra.ai/product/agent-studioread 2026-09-05

Work happens programmatically inside the customer's systems through controlled integrations, tapping into Zendesk, Stripe and internal APIs, with tool calls such as retrieving recent transactions shown as named workflow steps. No browser control, hosted session or computer use is described. Lorikeet's public HTTP endpoints let outside AI assistants call Lorikeet and do not operate any interface.

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Agents act on the customer's systems through API integrations built with the SDK, kept deterministic and bound by policy, and deploy across chat, phone, email, SMS and messaging. Browser control, a virtual desktop and operation of software without a programmatic interface are not part of the platform.

Sourcesierra.ai/product/agent-sdkread 2026-09-05