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Agent Gateway – Governed Execution of AI Agents in Production

Unified execution, observability, and control for AI agents through an enterprise-grade Agent Gateway

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Unified Agent Control

Run all AI agents through a single, governed execution layer with centralized policies and controls.

Agent Observability

Track every agent action, step, and decision with full traceability across models and tools.

Policy & RBAC Enforcement

Apply role-based access control and policies to govern who can deploy, run, or modify agents.

Agent Task Execution

Execute multi-step agent workflows reliably with retries, timeouts, and controlled execution paths.

Scalable & Reliable

Scale agent workloads automatically while maintaining predictable behavior under load.

Framework Agnostic

Compatible with any agent framework or custom implementation, optimized for production use.

Centralized Agent Registry

Run and govern AI agents through a single, enterprise-grade Agent Gateway.
  • Execute all agent workflows through a single Agent Gateway instead of embedding logic across applications
  • Support framework-agnostic agents, including LangChain, CrewAI, and fully custom agent implementations
  • Standardize how agents invoke LLMs and tools using consistent routing, policies, and execution rules
  • Centralize authentication, identity, and service account management for agents at the Gateway layer
MCP Gateway Server Registry

Agent Observability & Tracing

Understand how agents behave with full, step-level observability.
  • Monitor agent latency, error rates, retries, and tool invocations across all workflows
  • Capture end-to-end execution traces spanning agent steps, model calls, and tool interactions
  • Attribute token usage and cost to specific agents, workflows, teams, or environments
  • Inspect detailed execution logs to quickly diagnose failures and performance bottlenecks
MCP Gateway Tool Discovery for MCP servers

Agent Quotas, Budgets & Access Control

Protect budgets, enforce governance, and reduce risk for autonomous agents
  • Enforce token-based or cost-based quotas per agent, workflow, or environment
  • Apply role-based access control (RBAC) to restrict who can deploy, execute, or modify agents
  • Govern service accounts and autonomous agents using centralized identity and policy rules
  • Isolate development, staging, and production agent workloads with clear access boundaries
MCP Gateway Tool Discovery for MCP servers

Reliable Agent Execution, Retries & Fallbacks

Ensure agent workflows remain resilient under real-world failures.
  • Automatically retry failed agent steps with configurable retry policies
  • Define fallback paths for model calls or tool executions
  • Apply timeouts and safeguards to prevent infinite loops or stalled agents
  • Maintain consistent behavior during model outages, tool failures, or traffic spikes
MCP Gateway Tool Discovery for MCP servers

MCP-Powered Tool Execution for Agents

Secure and govern all agent tool calls using native MCP integration.
  • Route all agent tool calls through registered MCP Servers
  • Connect agents to enterprise tools such as Slack, GitHub, databases, and internal services
  • Apply OAuth2, RBAC, and metadata-based policies to every tool invocation
  • Audit and log all agent-initiated tool actions for security and compliance
MCP Gateway Tool Discovery for MCP servers

Guardrails for Autonomous Agents

Enforce safety, compliance, and behavioral controls for agent workflows.
  • Control which tools and capabilities each agent is allowed to access
  • Enforce safety policies such as PII filtering or restricted actions
  • Apply custom guardrails aligned with organizational compliance requirements
  • Maintain full audit trails for agent decisions and actions
MCP Server Connection Status

Made for Real-World AI at Scale

99.99%
uptime
Centralized failovers, routing, and guardrails ensure your AI apps stay online, even when model providers don’t.
10B+
Requests processed/month
Scalable, high-throughput inference for production AI.
30%
Average cost optimization
Smart routing, batching, and budget controls reduce token waste. 

Enterprise-Ready

Your data and models are securely housed within your cloud / on-prem infrastructure

  • Compliance & Security

    SOC 2, HIPAA, and GDPR standards to ensure robust data protection

  • Governance & Access Control

    SSO + Role-Based Access Control (RBAC) & Audit Logging

  • Enterprise Support & Reliability

    24/7 support with SLA-backed response SLAs
Deploy TrueFoundry in any environment

VPC, on-prem, air-gapped, or across multiple clouds.

No data leaves your domain. Enjoy complete sovereignty, isolation, and enterprise-grade compliance wherever TrueFoundry runs

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Real Outcomes at TrueFoundry

Why Enterprises Choose TrueFoundry

3x

faster time to value with autonomous LLM agents

80%

higher GPU‑cluster utilization after automated agent optimization

Aaron Erickson

Founder, Applied AI Lab

TrueFoundry turned our GPU fleet into an autonomous, self‑optimizing engine - driving 80 % more utilization and saving us millions in idle compute.

5x

faster time to productionize internal AI/ML platform

50%

lower cloud spend after migrating workloads to TrueFoundry

Pratik Agrawal

Sr. Director, Data Science & AI Innovation

TrueFoundry helped us move from experimentation to production in record time. What would've taken over a year was done in months - with better dev adoption.

80%

reduction in time-to-production for models

35%

cloud cost savings compared to the previous SageMaker setup

Vibhas Gejji

Staff ML Engineer

We cut DevOps burden and simplified production rollouts across teams. TrueFoundry accelerated ML delivery with infra that scales from experiments to robust services.

50%

faster RAG/Agent stack deployment

60%

reduction in maintenance overhead for RAG/agent pipelines

Indroneel G.

Intelligent Process Leader

TrueFoundry helped us deploy a full RAG stack - including pipelines, vector DBs, APIs, and UI—twice as fast with full control over self-hosted infrastructure.

60%

faster AI deployments

~40-50%

Effective Cost reduction of across dev environments

Nilav Ghosh

Senior Director, AI

With TrueFoundry, we reduced deployment timelines by over half and lowered infrastructure overhead through a unified MLOps interface—accelerating value delivery.

<2

weeks to migrate all production models

75%

reduction in data‑science coordination time, accelerating model updates and feature rollouts

Rajat Bansal

CTO

We saved big on infra costs and cut DS coordination time by 75%. TrueFoundry boosted our model deployment velocity across teams.

Frequently asked questions

How is an Agent Gateway different from an AI Gateway?

The AI Gateway is the umbrella layer that includes the LLM Gateway, MCP Gateway, and Agent Gateway. Together, these three form the core building blocks required to build and run AI agents in production.
The Agent Gateway sits on top, orchestrating agent workflows, while leveraging the LLM Gateway for model access and the MCP Gateway for secure tool execution.

Can I use the Agent Gateway with any agent framework?

An MCP Server (Model Context Protocol Server) is a standardized interface layer that wraps around enterprise APIs or tools, making them easily discoverable and callable by AI agents. When integrated with an MCP Gateway, each MCP Server registers itself, becomes accessible through a unified endpoint, and inherits enterprise-grade features like RBAC, federated authentication (via Okta, Azure AD), and observability, making orchestration across tools like Slack, Jira, or internal APIs effortless.

How does the Agent Gateway handle observability and cost tracking?

You can build an MCP Server using TrueFoundry’s SDK or your preferred backend stack. MCP Servers are containerized and typically deployed on Kubernetes or cloud-native infrastructure. Once live, they register with the MCP Gateway and are made available for secure discovery and task execution via agents or users, streamlining the AI integration pipeline.

Can I control and audit autonomous agents?

The MCP Gateway provides several key features for enterprises. It offers unified access to all registered MCP Servers, instant discovery via a central registry, and secure access control with OAuth 2.0 and federated identity providers. It enables agentic task execution across tools, offers enterprise-grade observability with request-level tracing and audit logs, supports out-of-the-box and custom integrations (e.g., Slack, Datadog, internal APIs, among others), and ensures high-performance operation across cloud, on-prem, and hybrid environments.

Is the Agent Gateway suitable for regulated or enterprise environments?

There are various benefits of using an MCP Gateway in enterprise environments. It dramatically simplifies tool integrations, accelerates onboarding via prebuilt MCP Servers, and unifies security and compliance controls. It enables plug-and-play agentic workflows, supports distributed environments, and provides deep observability for cost and performance. The result is a scalable, secure, and maintainable AI system capable of handling real-time enterprise workloads with minimal engineering effort.

How can I get started with the TrueFoundry Agent Gateway?

Authorization in an MCP Gateway is enforced through Role-Based Access Control (RBAC) policies integrated with enterprise Identity Providers such as Okta or Azure AD. Each MCP Server, endpoint, or tool function can be governed by specific access rules, ensuring only authorized users or agents can trigger actions or retrieve sensitive data.

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