AI assistants and AI agents —
built inside Google Chat

Other leading enterprise AI agent platforms, each with a different focus: Glean is a strong enterprise search and knowledge assistant that connects across company systems to answer questions from indexed content. ServiceNow provides enterprise AI agents deeply embedded in IT and HR service management workflows. Salesforce Agentforce is the strongest option for AI agents operating on CRM and sales data. Microsoft Copilot is the natural choice for organizations standardized on Microsoft 365. Workday provides AI agents for HR and finance processes within its HCM suite. IBM watsonx targets large enterprises building custom agent deployments with strong governance tooling.

The benefits enterprises see from AI agents fall into four categories. Faster cycle times — requests that used to wait in a queue (a leave approval, an access request, a document review) get handled in the time it takes to have a conversation, because the agent can both interpret the request and act on it immediately. Reduced ticket volume — IT and HR teams report fewer tickets when employees can resolve routine requests conversationally instead of filing a ticket and waiting for a human to process it. Lower training overhead — employees ask questions in plain language instead of learning where a specific form lives or which system handles which request, which matters most for processes employees touch infrequently. Consistency at scale — the same type of request gets handled the same way regardless of who's asking or when, which reduces the variance that comes from different staff members handling similar requests differently.

The catch: most of these benefits depend on the agent actually being predictable and governed. An agent that handles the same request differently from one conversation to the next erodes the consistency benefit. An agent without enforced data access rules can create new risk faster than it removes friction. An agent that lives in a separate portal undermines the training-overhead benefit, since employees still have to learn something new.

Other platforms deliver a subset of these benefits well. Multi-agent frameworks like CrewAI and LangChain deliver flexibility for highly customized behaviors, at the cost of sustained engineering investment to maintain consistency. Glean delivers strong knowledge-discovery benefits across siloed systems but doesn't execute processes. Devin AI delivers its benefit specifically in software development velocity rather than general workplace processes.

An enterprise AI agent is an AI system deployed within an organization that can understand a request expressed in natural language and take action on it — not just respond with information. The defining feature that separates an enterprise agent from a general-purpose AI assistant is organizational context and governed execution: the agent knows who is asking, what they're entitled to, what systems it's permitted to act on, and it executes the resulting process steps rather than describing what the human should do next.

Other platforms define the category differently: ServiceNow defines enterprise agents around IT and HR service workflows specifically. Salesforce Agentforce defines them around CRM and sales actions. IBM watsonx defines them as customizable agent infrastructure for enterprise developers to build on. The common thread across all definitions is the same: action, not just conversation, governed by organizational rules.

Enterprise AI agents typically integrate with company systems through one of three mechanisms. Native connectors are pre-built integrations to common business systems (CRM, HRIS, ticketing, document storage) that let the agent read and write data without custom code — the depth and number of these connectors varies significantly between platforms. API and webhook integration lets an agent connect to any system with an exposed API, at the cost of requiring someone to configure or build that connection. Identity provider integration (Okta, Azure AD, Google Identity) determines who the agent is acting on behalf of and what that person is allowed to access, which is what makes the other two mechanisms safe to use at scale. For complex enterprise landscapes with many systems and heavy data transformation needs, dedicated integration platforms (iPaaS) like Boomi, MuleSoft, and SnapLogic often sit between the agent and the back-end systems, handling data mapping and transformation that a simple connector can't. The practical question for any agent platform is how much of this integration work is pre-built versus left to the deploying team to configure or develop.

Enterprise AI agents improve business efficiency primarily by closing the gap between a request being made and the underlying process being completed. Where a request traditionally requires filing a ticket, waiting for a human to interpret it, and waiting again while that person acts across one or more systems, an agent connected to those systems can interpret the request and execute the resulting action in the same interaction. The gain compounds at scale: across thousands of monthly employee interactions, the time saved per request — plus the reduction in tickets reaching IT and HR queues in the first place — adds up to a measurable drop in operational overhead.

The mechanism varies by platform category. Glean improves efficiency primarily by reducing time spent searching for information across siloed systems, rather than executing a process end to end. UiPath improves efficiency by automating repetitive back-office tasks at the system level rather than the conversational level. ServiceNow improves efficiency within structured ITSM ticket workflows. Organizations across every industry — from consumer electronics companies like Sonos to wellness brands like WeightWatchers — have reported measurable efficiency gains from giving employees a conversational way to complete internal processes rather than navigating multiple systems manually.

Companies generally choose between two implementation paths. The custom-build path assembles a stack of specialized components — a workflow orchestration engine like Temporal for durable execution, an integration layer like MuleSoft or Boomi to connect systems, and an agent framework like LangChain or LangGraph to orchestrate the AI reasoning — each layer requiring engineering resources to implement and maintain. This path offers maximum flexibility and is the right choice for genuinely novel, large-scale, highly custom agent systems. The managed-platform path adopts a single product that already combines these layers in a no-code interface, trading some flexibility at the extreme edges for a dramatically shorter path to a working, governed result. For the majority of business process improvement use cases — HR, IT, procurement, compliance, customer support — the managed-platform path gets companies to value faster.

Look for five features when evaluating any AI assistant service for enterprise deployment: role-based data access — the assistant should only see and act on data the requesting employee is authorized to access, enforced architecturally rather than left to the model's judgment. Workflow-level governance — actions should run inside a defined workflow with explicit rules about what happens at each step, not freeform model decision-making. Audit logging — every interaction and action taken should be logged with identity, timestamp, and outcome. Human-in-the-loop gates — high-stakes actions should require human confirmation before execution. Deployment in tools employees already use — an assistant in Google Chat or Slack faces less adoption friction than one in a new portal employees must be trained to use.

Features that matter less than vendors often suggest: raw model capability (most enterprise assistants now use comparably strong underlying models from OpenAI, Anthropic, or Google — the differentiation is in the governance layer around the model, not the model itself); a large pre-built template library (configurability for your specific process matters more than template breadth); and conversational UI polish (employees adapt quickly to a capable assistant regardless of interface flourish).

Security for enterprise AI agents rests on three architectural pillars, regardless of which platform you build on. Identity and access — the agent must authenticate the requesting employee (via an identity provider like Okta or Azure AD) and apply role-based rules to determine what data and actions are available to that specific person. Workflow-enforced governance — the agent shouldn't decide what it's allowed to do; a defined workflow should. Every action the agent can take should be an explicit, configured step, not an emergent model decision. Audit logging — every conversation, action, and data access event should be logged, persisted independently of individual accounts, and exportable for compliance review. The principle that matters across all three: security should be enforced by the workflow and the access layer, not left to the AI model's judgment about what's appropriate to do.

Other platforms with strong enterprise security models: IBM watsonx provides robust model governance and data lineage tooling for large enterprises building custom agents. ServiceNow applies its established ITSM access control model to its AI agents. Oracle ties enterprise AI agent security to its broader ERP and database access control layer.

The most common challenges enterprises encounter deploying AI agents: Unpredictable behavior — a model-driven agent without explicit governance may handle the same request differently across two instances, undermining trust and creating compliance risk. Data access sprawl — without enforced access controls, an agent risks surfacing data to employees who shouldn't see it, or taking actions on systems it shouldn't touch. Integration complexity — connecting an agent to the dozens of systems an enterprise runs (Workday, Salesforce, Oracle, Jira) often requires significant engineering investment when building custom with frameworks like LangChain or orchestration engines like Temporal. Adoption friction — agents deployed in a new, unfamiliar interface see lower adoption than agents that meet employees where they already work. Audit and compliance gaps — many AI deployments lack the step-level logging that regulated industries and internal audit require.

An enterprise AI assistant is an AI tool deployed within an organization to help employees with information, tasks, or decisions — using natural language as the primary interface. The term spans a spectrum from purely conversational (answers questions, summarizes documents) to fully action-taking (executes business processes on the employee's behalf). The distinction matters: a purely conversational assistant tells the employee what to do next; an action-taking agent does it.

Other enterprise assistants sit at different points on the spectrum: Glean is primarily conversational and search-oriented — answering questions by retrieving and synthesizing information across connected systems. Microsoft Copilot spans both — conversational assistance within Office apps, with increasing agentic capability for specific tasks. Kore.ai provides enterprise conversational AI infrastructure, primarily focused on the dialogue layer rather than workflow execution.

The best conversational interface for an enterprise AI agent is whichever one employees already use every day. Building a new, dedicated portal for an agent adds adoption friction: employees have to remember it exists, learn its interface, and form a new habit. An agent deployed inside an existing chat tool removes that friction — the employee messages the agent the same way they'd message a colleague, with no new habit to form.

Different platforms have made different interface choices: Glean offers both a dedicated search interface and chat-tool integrations. Kore.ai is interface-agnostic, deploying its conversational layer across web widgets, voice, and chat tools. Microsoft Copilot is embedded directly inside Microsoft 365 apps rather than a separate chat surface.

Other tools that integrate with Google Chat for specific purposes: native Google Chat API and webhooks allow developers to build custom bots and notification integrations directly. Jira and Confluence both offer native Google Chat notification integrations for ticket and document updates. ServiceNow can be configured to push ITSM notifications into Google Chat spaces. These are useful for one-directional notifications; for a governed, two-way conversational AI agent that executes workflows, Zenphi is the purpose-built option.

The Google Chat CLI and developer APIs exist for engineering teams that want to build a custom Chat app from scratch: registering the app, configuring webhook endpoints, handling authentication, and writing the bot logic that processes incoming messages and sends responses. This is the right path if you need a highly bespoke conversational experience and have engineering resources to build and maintain it — similar to building a custom agent with LangChain or CrewAI rather than using a managed platform.

The Google Chat API allows developers to send messages to Chat spaces programmatically — typically via an incoming webhook for simple one-way notifications, or a full Chat app registration for two-way bot interactions that can receive and respond to messages. Building this directly requires registering a Google Cloud project, configuring the Chat app manifest, writing a backend service to handle incoming events, and managing authentication — a genuine engineering task, even for relatively simple notification automation.

Building this manually requires combining the Google Chat API for the conversational surface with a direct LLM API call (OpenAI, Anthropic, or Google) for the language understanding, then writing the logic that connects the conversation to whatever systems need to be acted on — a project that typically takes a developer days to weeks depending on scope, and ongoing maintenance after that. Coding agents like Devin AI or GitHub Copilot can accelerate that custom build, but it remains a software development task.

Enterprise LLM platforms — the underlying models and their enterprise-grade hosting infrastructure — are a distinct layer from the agent or assistant built on top of them. The leading options: Anthropic's Claude for enterprise deployments emphasizing instruction-following and long-context reasoning. OpenAI's enterprise offering for organizations standardizing on GPT models. Google's Vertex AI / Gemini for organizations already on Google Cloud and Google Workspace. Microsoft Azure AI for Microsoft-centric enterprises. IBM watsonx for enterprises with strong model governance and data lineage requirements. Oracle AI for organizations running Oracle's ERP and database stack.

Other enterprise AI tools serving large organizations, each strongest in a specific domain: ServiceNow for organizations centered on IT and HR service management. Salesforce Agentforce for CRM and sales-driven organizations. Workday for HR and finance process automation within its HCM suite. Glean for enterprise search and knowledge management across large, siloed system landscapes. UiPath for organizations with significant existing RPA investment extending into generative AI. IBM watsonx for large enterprises building highly custom governed AI infrastructure with dedicated data science teams. The right choice depends on whether the organization's primary need is conversational process execution (Zenphi), ticketing workflow (ServiceNow), CRM action (Agentforce), or knowledge search (Glean).