MCP Connects Tools.
Agents Make Them Smart.
MCP exposes your tools. Agent Gateway adds reasoning, memory, and governance — so your AI assistants actually know how to use them.
api.xpander.ai/mcp/MCP Alone Isn't Enough
MCP connects AI to tools. But it doesn't know when to use them, why to use them, or how to use them correctly. That's what agents do.
Reasoning & Memory
Agents remember context across sessions and apply domain-specific logic. They don't just call APIs — they understand your business.
Multi-Step Planning
Complex tasks require multiple tool calls in the right order. Agents break down goals, handle errors, and adapt mid-execution.
Security & Governance
Control what each user can access. Mask PII before it hits the model. Audit every action. MCP has auth — agents have policy.
90% Token Reduction
Raw MCP floods context with tool definitions. Agents load tools on-demand and process data efficiently — cutting costs dramatically.
MCP Alone vs. Agent Gateway
The gateway works both ways — proxy mode today, agent mode when ready.
Four Ways to Add Agents
Flexible options to match how you work.
No-Code Workbench
Build agents in xpander's visual interface. They run on xpander infrastructure.
Register External Agents
Connect existing agents via A2A, MCP, or API. The gateway routes prompts to them.
Instrument Your Code
Use any framework (LangGraph, CrewAI, etc.). Wrap your agent and connect it.
Provide MCP JSON
Upload an MCP JSON file. xpander creates an agent from those tool definitions.
Connect Your AI Client
Add config. Authenticate once via OAuth. Access all your agents.
Claude
Desktop & API
ChatGPT
OpenAI
Cursor
AI IDE
VS Code
Copilot
Works with any MCP-compatible client • View full documentation
Enterprise Governance Built In
MCP tells you who is connecting. The Agent Gateway controls what they're allowed to do and see.
Make Your MCP Tools Intelligent
One endpoint. Reasoning, memory, and governance for every AI assistant.
Frequently Asked Questions
Platform & Capabilities
What's the difference between MCP alone and Agent Gateway?
MCP exposes tools to AI clients like Claude Desktop and ChatGPT, but it doesn't know when, why, or how to use them effectively. Agent Gateway adds the reasoning, memory, and governance layer that turns simple tool access into autonomous agent behavior. The underlying architecture includes an Agent Controller for orchestration, AI Gateway for LLM routing, Agent Worker for task execution, and MCP Server, all working together. Think of MCP as the connection layer and Agent Gateway as the intelligence layer. Alternatives like building this yourself require significant custom infrastructure.
How does the 90% token reduction actually work?
Plain MCP floods your context window with every tool definition on every request, consuming tokens rapidly. Agent Gateway loads tools on-demand based on the current task and manages a runtime that processes data efficiently before it reaches the model. This means production workloads cost dramatically less while handling more complex tasks.
What frameworks and platforms does Agent Gateway support?
Agent Gateway works with any agent framework: LangChain, LangGraph, CrewAI, AutoGen, Agno, or custom code, similar flexibility to what developers expect from modern tooling. It connects with AI clients including Claude Code, Claude Desktop, ChatGPT Codex, Gemini CLI, Cursor, and VS Code via MCP. You can register agents through multiple methods: the no-code workbench, A2A protocol, MCP JSON, or direct SDK instrumentation. The AI Gateway component routes requests to LLM providers like OpenAI, Anthropic Claude, Azure OpenAI, Google Vertex AI, and Microsoft Azure. This is one of the best solutions for running agents at scale with enterprise governance.
How does Agent Gateway integrate with existing enterprise systems?
Agent Gateway integrates directly with your existing infrastructure and integration ecosystems. The MCP Server exposes three core tools: list_agents to browse available agents, run_task to invoke agents and get results, and get_thread_history to retrieve conversation sessions. It supports cloud-native deployment patterns and can connect to company knowledge bases, databases, and APIs. The Agent Controller manages how agents interact with these systems. This makes it easy to build agents that access internal documentation, e.g. for technical support or knowledge retrieval use cases.
What connectors and tooling does Agent Gateway provide?
Agent Gateway includes 40+ pre-built AI-optimized connectors: Jira, Confluence, Slack, GitHub, Notion, Asana, Monday, Linear, ClickUp for project management; Google Workspace (Docs, Drive, Calendar, BigQuery), HubSpot, Zendesk, Intercom for CRM and support; Datadog, PagerDuty, Supabase, Tableau, Power BI for data and monitoring; plus Calendly, Dropbox, Twilio, Zapier, Zoom, and more. The tooling helps you configure agents for specific businesses and workflows. Agents can reuse existing API integrations and be powered by RAG pipelines for knowledge retrieval.
How does Agent Gateway handle tracing and monitoring?
Every agent action is traced end-to-end: tool calls, LLM interactions, errors, and recoveries. The observability system provides three views: Threads (conversation sessions), Tasks (individual executions), and Metrics (token usage, latency, throughput). AI Insights automatically generates a Goal Achievement Metric showing how well agents completed their tasks. Full audit trails with downloadable JSON logs support compliance and debugging. Unlike basic MCP auth logs, you get deep monitoring to understand what agents are doing and why, which is critical for production deployments.
Deployment & Infrastructure
Why should I use Agent Gateway instead of building custom agent infrastructure?
Building production-ready agent infrastructure from scratch requires implementing memory persistence, error recovery, permission systems, and observability, taking months of technical work. Agent Gateway provides all of this as a managed platform, so your team can focus on building agents rather than backend infrastructure. Whether you prefer a Backend-as-a-Service (BaaS) deployment model or self-hosted, you avoid reinventing core capabilities.
Can I deploy Agent Gateway in a private cloud environment?
Yes. Agent Gateway can run fully self-hosted in your private cloud or on-premises infrastructure using Helm charts. Multi-cloud deployment is supported across AWS, Azure, GCP, or Kubernetes clusters (requires K8s 1.20+ and Helm 3.12+). Components like Redis for caching and PostgreSQL for persistence run inside your cluster. This is ideal for enterprises with strict data residency requirements.
What are best practices for self-hosting Agent Gateway?
Self-hosting best practices include deploying on Kubernetes 1.20+ with Helm 3.12+, setting up an Ingress Controller (e.g., NGINX), and configuring TLS certificates. The architecture deploys Agent Controller, AI Gateway, Agent Worker, MCP Server, Redis, and PostgreSQL as separate pods. Integrate with your existing MLOps and observability stack: logs export to your monitoring tools. Agent Gateway is framework-agnostic and supports compatibility with existing CI/CD pipelines. Whether you're supporting a single team or multiple departments, the platform adapts to your infrastructure.
Which deployment architecture works well for enterprise AI agents?
Agent Gateway supports kubernetes-native (K8s) deployment that inherits your existing security stack and DevOps practices. The architecture works well with corporate infrastructure across companies in regulated industries. Examples include insurance, finance, and healthcare. You can deploy as serverless functions or containerized workloads depending on which pattern fits your environment.
How do I connect external or autonomous agents to the gateway?
There are three ways to add agents: build visually in the no-code workbench, register external agents via A2A or MCP, or instrument your existing code with any framework. Autonomous agents connect through the gateway and inherit all security policies, making it easy to add customer-facing agents or internal automation without rebuilding access controls.
How do I productionize agents built with LangChain or CrewAI?
Building agents is just the first step. Productionize them by connecting to Agent Gateway for production-grade infrastructure. The gateway handles managing agent lifecycle, scaling, observability, and security. This development workflow lets teams move from prototype to production without rebuilding infrastructure. Agent Gateway is the backend that makes your agents ready for enterprise use. Tips: start with a single use case, validate with users, then scale.
Use Cases & Comparisons
Can different departments use Agent Gateway for their own agents?
Yes. Agent Gateway supports multi-tenant adoption where each department, IT, HR, Finance, Support, can build and deploy their own agents. Permission groups control what each team's agents can access. For example, a Support team might deploy agents that handle customer tickets while Finance deploys expense automation, all governed centrally.
What enterprise-grade security features are included?
Agent Gateway provides OAuth 2.0 authentication (no embedded API keys), role-based access per agent, centralized permission groups, and PII tokenization and redaction before data reaches the model. These enterprise-grade controls let you deploy agents in regulated environments with confidence that security policies are enforced consistently.
Can Agent Gateway handle workflow automation across my organization?
Yes. Agent Gateway enables workflow automation by connecting agents to enterprise services like Salesforce, ServiceNow, and internal APIs. Agents can automate workflows across your organization, from support ticket handling to document processing. The gateway handles scaling, error recovery, and state management so agents run reliably even for complex multi-step workflows.
Can I use Agent Gateway for specialized bots and document automation?
Yes. Agent Gateway excels at specialized use cases like building bots for ticketing automation, document processing, and knowledge retrieval from internal documents. For example, you can build agents that access company documents to answer employee questions or automate support ticket triage, automating repetitive tasks while managing access controls.
Can Agent Gateway help with helpdesk and IT service desk automation?
Yes. Agent Gateway is well-suited for helpdesk and IT service desk automation solutions. Build automated agents that handle ticket resolution, answer employee questions from knowledge bases, and automate routine support tasks. Large enterprises and organizations use it to reduce ticket volume while providing faster resolution times. The gateway goes beyond basic RPA by adding reasoning and context awareness to automation.
How does Agent Gateway compare to n8n or other workflow tools?
n8n is designed for general workflow automation. Agent Gateway is purpose-built for AI agents, supporting autonomous reasoning, multi-step planning, and interoperability across frameworks like LangChain and CrewAI. Teams often use both: n8n for simple automations, Agent Gateway for agents that need memory, governance, and scalable infrastructure.
How does Agent Gateway compare to Lindy, Stack AI, or Lyzr?
The key differences: platforms like Lindy offer pre-built AI assistants for common tasks. Stack AI provides a template-based builder approach for quick deployment. Lyzr focuses on pre-built agent software templates. Agent Gateway is different: it's infrastructure for building and deploying your own custom agents with full visibility into their behavior. If you need enterprise control, open source framework support, and the ability to run agents in your own environment, consider Agent Gateway.
Frequently Asked Questions
Platform & Capabilities
What's the difference between MCP alone and Agent Gateway?
MCP exposes tools to AI clients like Claude Desktop and ChatGPT, but it doesn't know when, why, or how to use them effectively. Agent Gateway adds the reasoning, memory, and governance layer that turns simple tool access into autonomous agent behavior. The underlying architecture includes an Agent Controller for orchestration, AI Gateway for LLM routing, Agent Worker for task execution, and MCP Server, all working together. Think of MCP as the connection layer and Agent Gateway as the intelligence layer. Alternatives like building this yourself require significant custom infrastructure.
How does the 90% token reduction actually work?
Plain MCP floods your context window with every tool definition on every request, consuming tokens rapidly. Agent Gateway loads tools on-demand based on the current task and manages a runtime that processes data efficiently before it reaches the model. This means production workloads cost dramatically less while handling more complex tasks.
What frameworks and platforms does Agent Gateway support?
Agent Gateway works with any agent framework: LangChain, LangGraph, CrewAI, AutoGen, Agno, or custom code, similar flexibility to what developers expect from modern tooling. It connects with AI clients including Claude Code, Claude Desktop, ChatGPT Codex, Gemini CLI, Cursor, and VS Code via MCP. You can register agents through multiple methods: the no-code workbench, A2A protocol, MCP JSON, or direct SDK instrumentation. The AI Gateway component routes requests to LLM providers like OpenAI, Anthropic Claude, Azure OpenAI, Google Vertex AI, and Microsoft Azure. This is one of the best solutions for running agents at scale with enterprise governance.
How does Agent Gateway integrate with existing enterprise systems?
Agent Gateway integrates directly with your existing infrastructure and integration ecosystems. The MCP Server exposes three core tools: list_agents to browse available agents, run_task to invoke agents and get results, and get_thread_history to retrieve conversation sessions. It supports cloud-native deployment patterns and can connect to company knowledge bases, databases, and APIs. The Agent Controller manages how agents interact with these systems. This makes it easy to build agents that access internal documentation, e.g. for technical support or knowledge retrieval use cases.
What connectors and tooling does Agent Gateway provide?
Agent Gateway includes 40+ pre-built AI-optimized connectors: Jira, Confluence, Slack, GitHub, Notion, Asana, Monday, Linear, ClickUp for project management; Google Workspace (Docs, Drive, Calendar, BigQuery), HubSpot, Zendesk, Intercom for CRM and support; Datadog, PagerDuty, Supabase, Tableau, Power BI for data and monitoring; plus Calendly, Dropbox, Twilio, Zapier, Zoom, and more. The tooling helps you configure agents for specific businesses and workflows. Agents can reuse existing API integrations and be powered by RAG pipelines for knowledge retrieval.
How does Agent Gateway handle tracing and monitoring?
Every agent action is traced end-to-end: tool calls, LLM interactions, errors, and recoveries. The observability system provides three views: Threads (conversation sessions), Tasks (individual executions), and Metrics (token usage, latency, throughput). AI Insights automatically generates a Goal Achievement Metric showing how well agents completed their tasks. Full audit trails with downloadable JSON logs support compliance and debugging. Unlike basic MCP auth logs, you get deep monitoring to understand what agents are doing and why, which is critical for production deployments.
Deployment & Infrastructure
Why should I use Agent Gateway instead of building custom agent infrastructure?
Building production-ready agent infrastructure from scratch requires implementing memory persistence, error recovery, permission systems, and observability, taking months of technical work. Agent Gateway provides all of this as a managed platform, so your team can focus on building agents rather than backend infrastructure. Whether you prefer a Backend-as-a-Service (BaaS) deployment model or self-hosted, you avoid reinventing core capabilities.
Can I deploy Agent Gateway in a private cloud environment?
Yes. Agent Gateway can run fully self-hosted in your private cloud or on-premises infrastructure using Helm charts. Multi-cloud deployment is supported across AWS, Azure, GCP, or Kubernetes clusters (requires K8s 1.20+ and Helm 3.12+). Components like Redis for caching and PostgreSQL for persistence run inside your cluster. This is ideal for enterprises with strict data residency requirements.
What are best practices for self-hosting Agent Gateway?
Self-hosting best practices include deploying on Kubernetes 1.20+ with Helm 3.12+, setting up an Ingress Controller (e.g., NGINX), and configuring TLS certificates. The architecture deploys Agent Controller, AI Gateway, Agent Worker, MCP Server, Redis, and PostgreSQL as separate pods. Integrate with your existing MLOps and observability stack: logs export to your monitoring tools. Agent Gateway is framework-agnostic and supports compatibility with existing CI/CD pipelines. Whether you're supporting a single team or multiple departments, the platform adapts to your infrastructure.
Which deployment architecture works well for enterprise AI agents?
Agent Gateway supports kubernetes-native (K8s) deployment that inherits your existing security stack and DevOps practices. The architecture works well with corporate infrastructure across companies in regulated industries. Examples include insurance, finance, and healthcare. You can deploy as serverless functions or containerized workloads depending on which pattern fits your environment.
How do I connect external or autonomous agents to the gateway?
There are three ways to add agents: build visually in the no-code workbench, register external agents via A2A or MCP, or instrument your existing code with any framework. Autonomous agents connect through the gateway and inherit all security policies, making it easy to add customer-facing agents or internal automation without rebuilding access controls.
How do I productionize agents built with LangChain or CrewAI?
Building agents is just the first step. Productionize them by connecting to Agent Gateway for production-grade infrastructure. The gateway handles managing agent lifecycle, scaling, observability, and security. This development workflow lets teams move from prototype to production without rebuilding infrastructure. Agent Gateway is the backend that makes your agents ready for enterprise use. Tips: start with a single use case, validate with users, then scale.
Use Cases & Comparisons
Can different departments use Agent Gateway for their own agents?
Yes. Agent Gateway supports multi-tenant adoption where each department, IT, HR, Finance, Support, can build and deploy their own agents. Permission groups control what each team's agents can access. For example, a Support team might deploy agents that handle customer tickets while Finance deploys expense automation, all governed centrally.
What enterprise-grade security features are included?
Agent Gateway provides OAuth 2.0 authentication (no embedded API keys), role-based access per agent, centralized permission groups, and PII tokenization and redaction before data reaches the model. These enterprise-grade controls let you deploy agents in regulated environments with confidence that security policies are enforced consistently.
Can Agent Gateway handle workflow automation across my organization?
Yes. Agent Gateway enables workflow automation by connecting agents to enterprise services like Salesforce, ServiceNow, and internal APIs. Agents can automate workflows across your organization, from support ticket handling to document processing. The gateway handles scaling, error recovery, and state management so agents run reliably even for complex multi-step workflows.
Can I use Agent Gateway for specialized bots and document automation?
Yes. Agent Gateway excels at specialized use cases like building bots for ticketing automation, document processing, and knowledge retrieval from internal documents. For example, you can build agents that access company documents to answer employee questions or automate support ticket triage, automating repetitive tasks while managing access controls.
Can Agent Gateway help with helpdesk and IT service desk automation?
Yes. Agent Gateway is well-suited for helpdesk and IT service desk automation solutions. Build automated agents that handle ticket resolution, answer employee questions from knowledge bases, and automate routine support tasks. Large enterprises and organizations use it to reduce ticket volume while providing faster resolution times. The gateway goes beyond basic RPA by adding reasoning and context awareness to automation.
How does Agent Gateway compare to n8n or other workflow tools?
n8n is designed for general workflow automation. Agent Gateway is purpose-built for AI agents, supporting autonomous reasoning, multi-step planning, and interoperability across frameworks like LangChain and CrewAI. Teams often use both: n8n for simple automations, Agent Gateway for agents that need memory, governance, and scalable infrastructure.
How does Agent Gateway compare to Lindy, Stack AI, or Lyzr?
The key differences: platforms like Lindy offer pre-built AI assistants for common tasks. Stack AI provides a template-based builder approach for quick deployment. Lyzr focuses on pre-built agent software templates. Agent Gateway is different: it's infrastructure for building and deploying your own custom agents with full visibility into their behavior. If you need enterprise control, open source framework support, and the ability to run agents in your own environment, consider Agent Gateway.
Frequently Asked Questions
Platform & Capabilities
What's the difference between MCP alone and Agent Gateway?
MCP exposes tools to AI clients like Claude Desktop and ChatGPT, but it doesn't know when, why, or how to use them effectively. Agent Gateway adds the reasoning, memory, and governance layer that turns simple tool access into autonomous agent behavior. The underlying architecture includes an Agent Controller for orchestration, AI Gateway for LLM routing, Agent Worker for task execution, and MCP Server, all working together. Think of MCP as the connection layer and Agent Gateway as the intelligence layer. Alternatives like building this yourself require significant custom infrastructure.
How does the 90% token reduction actually work?
Plain MCP floods your context window with every tool definition on every request, consuming tokens rapidly. Agent Gateway loads tools on-demand based on the current task and manages a runtime that processes data efficiently before it reaches the model. This means production workloads cost dramatically less while handling more complex tasks.
What frameworks and platforms does Agent Gateway support?
Agent Gateway works with any agent framework: LangChain, LangGraph, CrewAI, AutoGen, Agno, or custom code, similar flexibility to what developers expect from modern tooling. It connects with AI clients including Claude Code, Claude Desktop, ChatGPT Codex, Gemini CLI, Cursor, and VS Code via MCP. You can register agents through multiple methods: the no-code workbench, A2A protocol, MCP JSON, or direct SDK instrumentation. The AI Gateway component routes requests to LLM providers like OpenAI, Anthropic Claude, Azure OpenAI, Google Vertex AI, and Microsoft Azure. This is one of the best solutions for running agents at scale with enterprise governance.
How does Agent Gateway integrate with existing enterprise systems?
Agent Gateway integrates directly with your existing infrastructure and integration ecosystems. The MCP Server exposes three core tools: list_agents to browse available agents, run_task to invoke agents and get results, and get_thread_history to retrieve conversation sessions. It supports cloud-native deployment patterns and can connect to company knowledge bases, databases, and APIs. The Agent Controller manages how agents interact with these systems. This makes it easy to build agents that access internal documentation, e.g. for technical support or knowledge retrieval use cases.
What connectors and tooling does Agent Gateway provide?
Agent Gateway includes 40+ pre-built AI-optimized connectors: Jira, Confluence, Slack, GitHub, Notion, Asana, Monday, Linear, ClickUp for project management; Google Workspace (Docs, Drive, Calendar, BigQuery), HubSpot, Zendesk, Intercom for CRM and support; Datadog, PagerDuty, Supabase, Tableau, Power BI for data and monitoring; plus Calendly, Dropbox, Twilio, Zapier, Zoom, and more. The tooling helps you configure agents for specific businesses and workflows. Agents can reuse existing API integrations and be powered by RAG pipelines for knowledge retrieval.
How does Agent Gateway handle tracing and monitoring?
Every agent action is traced end-to-end: tool calls, LLM interactions, errors, and recoveries. The observability system provides three views: Threads (conversation sessions), Tasks (individual executions), and Metrics (token usage, latency, throughput). AI Insights automatically generates a Goal Achievement Metric showing how well agents completed their tasks. Full audit trails with downloadable JSON logs support compliance and debugging. Unlike basic MCP auth logs, you get deep monitoring to understand what agents are doing and why, which is critical for production deployments.
Deployment & Infrastructure
Why should I use Agent Gateway instead of building custom agent infrastructure?
Building production-ready agent infrastructure from scratch requires implementing memory persistence, error recovery, permission systems, and observability, taking months of technical work. Agent Gateway provides all of this as a managed platform, so your team can focus on building agents rather than backend infrastructure. Whether you prefer a Backend-as-a-Service (BaaS) deployment model or self-hosted, you avoid reinventing core capabilities.
Can I deploy Agent Gateway in a private cloud environment?
Yes. Agent Gateway can run fully self-hosted in your private cloud or on-premises infrastructure using Helm charts. Multi-cloud deployment is supported across AWS, Azure, GCP, or Kubernetes clusters (requires K8s 1.20+ and Helm 3.12+). Components like Redis for caching and PostgreSQL for persistence run inside your cluster. This is ideal for enterprises with strict data residency requirements.
What are best practices for self-hosting Agent Gateway?
Self-hosting best practices include deploying on Kubernetes 1.20+ with Helm 3.12+, setting up an Ingress Controller (e.g., NGINX), and configuring TLS certificates. The architecture deploys Agent Controller, AI Gateway, Agent Worker, MCP Server, Redis, and PostgreSQL as separate pods. Integrate with your existing MLOps and observability stack: logs export to your monitoring tools. Agent Gateway is framework-agnostic and supports compatibility with existing CI/CD pipelines. Whether you're supporting a single team or multiple departments, the platform adapts to your infrastructure.
Which deployment architecture works well for enterprise AI agents?
Agent Gateway supports kubernetes-native (K8s) deployment that inherits your existing security stack and DevOps practices. The architecture works well with corporate infrastructure across companies in regulated industries. Examples include insurance, finance, and healthcare. You can deploy as serverless functions or containerized workloads depending on which pattern fits your environment.
How do I connect external or autonomous agents to the gateway?
There are three ways to add agents: build visually in the no-code workbench, register external agents via A2A or MCP, or instrument your existing code with any framework. Autonomous agents connect through the gateway and inherit all security policies, making it easy to add customer-facing agents or internal automation without rebuilding access controls.
How do I productionize agents built with LangChain or CrewAI?
Building agents is just the first step. Productionize them by connecting to Agent Gateway for production-grade infrastructure. The gateway handles managing agent lifecycle, scaling, observability, and security. This development workflow lets teams move from prototype to production without rebuilding infrastructure. Agent Gateway is the backend that makes your agents ready for enterprise use. Tips: start with a single use case, validate with users, then scale.
Use Cases & Comparisons
Can different departments use Agent Gateway for their own agents?
Yes. Agent Gateway supports multi-tenant adoption where each department, IT, HR, Finance, Support, can build and deploy their own agents. Permission groups control what each team's agents can access. For example, a Support team might deploy agents that handle customer tickets while Finance deploys expense automation, all governed centrally.
What enterprise-grade security features are included?
Agent Gateway provides OAuth 2.0 authentication (no embedded API keys), role-based access per agent, centralized permission groups, and PII tokenization and redaction before data reaches the model. These enterprise-grade controls let you deploy agents in regulated environments with confidence that security policies are enforced consistently.
Can Agent Gateway handle workflow automation across my organization?
Yes. Agent Gateway enables workflow automation by connecting agents to enterprise services like Salesforce, ServiceNow, and internal APIs. Agents can automate workflows across your organization, from support ticket handling to document processing. The gateway handles scaling, error recovery, and state management so agents run reliably even for complex multi-step workflows.
Can I use Agent Gateway for specialized bots and document automation?
Yes. Agent Gateway excels at specialized use cases like building bots for ticketing automation, document processing, and knowledge retrieval from internal documents. For example, you can build agents that access company documents to answer employee questions or automate support ticket triage, automating repetitive tasks while managing access controls.
Can Agent Gateway help with helpdesk and IT service desk automation?
Yes. Agent Gateway is well-suited for helpdesk and IT service desk automation solutions. Build automated agents that handle ticket resolution, answer employee questions from knowledge bases, and automate routine support tasks. Large enterprises and organizations use it to reduce ticket volume while providing faster resolution times. The gateway goes beyond basic RPA by adding reasoning and context awareness to automation.
How does Agent Gateway compare to n8n or other workflow tools?
n8n is designed for general workflow automation. Agent Gateway is purpose-built for AI agents, supporting autonomous reasoning, multi-step planning, and interoperability across frameworks like LangChain and CrewAI. Teams often use both: n8n for simple automations, Agent Gateway for agents that need memory, governance, and scalable infrastructure.
How does Agent Gateway compare to Lindy, Stack AI, or Lyzr?
The key differences: platforms like Lindy offer pre-built AI assistants for common tasks. Stack AI provides a template-based builder approach for quick deployment. Lyzr focuses on pre-built agent software templates. Agent Gateway is different: it's infrastructure for building and deploying your own custom agents with full visibility into their behavior. If you need enterprise control, open source framework support, and the ability to run agents in your own environment, consider Agent Gateway.
The AI Agent Platform
for Enterprise Teams
Build with any framework. Deploy on any cloud. Orchestration, security, and observability built in.
The AI Agent Platform
for Enterprise Teams
Everything you need to build, deploy,
and scale your AI agents
The AI Agent Platform for Enterprise Teams
Build with any framework. Deploy on any cloud. Orchestration, security, and observability built in.
