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Claude Skills: How Anthropic's Agent Skills Work

Claude Skills: How Anthropic's Agent Skills Work

Claude Skills are modular capabilities that extend Claude with domain expertise. Learn how Agent Skills work, where they run, and how to evaluate them. Claude Skills are reusable capability packages that let Anthropic's Claude perform specialized tasks consistently across products. Introduced in late 2025, Claude Skills (also called

OpenRouter vs LiteLLM vs Bifrost: AI Gateway Comparison

OpenRouter vs LiteLLM vs Bifrost: AI Gateway Comparison

Compare OpenRouter vs LiteLLM vs Bifrost on performance, governance, MCP support, and deployment. Find the right AI gateway for production workloads. Teams running production AI workloads almost always end up evaluating an AI gateway. A direct integration with one provider works for a prototype, but it breaks down the moment

Self-Hosted AI Gateway for Cursor with Claude or Ollama

Self-Hosted AI Gateway for Cursor with Claude or Ollama

Run Cursor with Claude, Ollama, and 20+ providers through a self-hosted AI gateway. Configure custom models, virtual keys, and unified routing with Bifrost. Engineering teams adopting Cursor as their primary AI IDE quickly hit the limits of its default model picker. Cost overruns from agent-mode runs, model lock-in to a

Tracing AI Agent Failures: Debugging Multi-Step Tool Workflows

Tracing AI Agent Failures: Debugging Multi-Step Tool Workflows

When AI agents fail in production, logs alone are not enough. Learn how distributed tracing exposes failure points across multi-step tool workflows. When a deterministic service breaks, you get a stack trace. When an AI agent breaks, you get a clean response that is silently wrong. Tracing AI agent failures

Connect Claude Code to Multiple MCP Servers Through One Gateway

Connect Claude Code to Multiple MCP Servers Through One Gateway

Use Bifrost to connect Claude Code to multiple MCP servers through a single /mcp endpoint, with centralized tool governance, audit logs, and lower token costs. Connecting Claude Code to multiple MCP servers gets expensive fast. Every server you add to Claude Code is a standalone connection with its own credentials,

Code Execution with MCP: How Code Mode Cuts Agent Token Costs by 90%+

Code Execution with MCP: How Code Mode Cuts Agent Token Costs by 90%+

Code execution with MCP replaces direct tool calls with Python orchestration, cutting AI agent token costs by up to 92.8% while preserving full tool access. Code execution with MCP is the architectural shift that lets AI agents scale across hundreds of tools without burning their context budget on tool

Manual Approval vs Autonomous Tool Execution: Designing Safe AI Agent Loops

Manual Approval vs Autonomous Tool Execution: Designing Safe AI Agent Loops

Designing safe AI agent loops means choosing where to gate tool execution. A practical guide to manual approval, autonomous execution, and the middle path. Every production AI agent eventually has to answer the same question: when the model proposes a tool call, who decides whether it runs? The two ends

MCP Tool Groups: Managing AI Tool Access at Scale

MCP Tool Groups: Managing AI Tool Access at Scale

Bifrost MCP Tool Groups let platform teams govern MCP tool access across teams, customers, users, and providers from a single named policy. Once an MCP deployment moves past a handful of virtual keys, managing tool access one key at a time stops working. A new team needs the same database

MCP RBAC: Tool-Level Permissions for Production AI Agents

MCP RBAC: Tool-Level Permissions for Production AI Agents

MCP RBAC enforces tool-level permissions for production AI agents. Learn how Bifrost's virtual keys and tool filtering deliver least-privilege access at scale. Production AI agents now connect to dozens of Model Context Protocol (MCP) servers exposing hundreds of tools, yet most setups still rely on a single credential

Top 5 MCP Gateways in 2026

Top 5 MCP Gateways in 2026

Compare the top 5 MCP gateways in 2026 on performance, governance, security, and deployment flexibility for production AI agents. The top MCP gateways in 2026 share one job: route, govern, and audit every tool call between AI agents and the Model Context Protocol servers behind them. With MCP now the

Why Production MCP Deployments Need an MCP Gateway

Why Production MCP Deployments Need an MCP Gateway

An MCP gateway provides production teams with auth, cost governance, and tool access control. See why direct MCP integrations break at enterprise scale. Production MCP deployments fail in predictable ways. A team wires three Model Context Protocol servers into an agent during prototyping. It works. They add five more for

AI Governance for Enterprise LLM Deployments: A Complete Guide

AI Governance for Enterprise LLM Deployments: A Complete Guide

A complete guide to AI governance for enterprise LLM deployments, covering access control, cost management, and compliance enforced at the gateway layer. Enterprise teams running large language models in production face a governance gap that no policy document can close. AI governance for enterprise LLM deployments has shifted from voluntary

What is an AI Gateway? A Complete Architecture Guide for Enterprise AI

What is an AI Gateway? A Complete Architecture Guide for Enterprise AI

An AI gateway is the control plane for enterprise LLM traffic. Learn how AI gateway architecture handles routing, governance, caching, and observability at scale. Enterprise AI teams are no longer running a single model against a single provider. Production stacks now mix OpenAI, Anthropic, AWS Bedrock, Google Vertex, and open-source

10 Key Strategies to Improve the Reliability of AI Agents in Production

10 Key Strategies to Improve the Reliability of AI Agents in Production

TLDR: Building reliable AI agents in production requires a comprehensive approach that extends far beyond initial development. Studies on ML systems show that 91% experience performance degradation over time, making continuous monitoring and proactive intervention essential. This guide covers 10 proven strategies for maintaining AI agent reliability, from implementing robust