Ai-Agents

Problem Definition As Model Context Protocol (MCP) emerges as the standard for AI agent connections, developers face new challenges: Testing Difficulty: No standard way to test MCP server tools, resources, and prompts Debugging Complexity: Need to support multiple transport protocols (STDIO, SSE, HTTP) Official Tool Gaps: Anthropic’s official inspector has limited features and slow development In 2026, the MCP market is exploding with 97M+ monthly SDK downloads. Market Analysis Metric Value MCP Market Size (2025) $1.8B Monthly SDK Downloads 97M+ Major Adopters Microsoft, Google, Salesforce, SAP Target Customers: Developers building MCP servers, AI agent builders ...

Problem Definition As AI agents reach production, new quality challenges emerge: Agent Hallucinations: Unlike LLM hallucinations, errors compound in multi-step processes No Runtime Verification: Lack of validation before responses reach users Quality Issues: 89% of organizations adopt observability, 32% cite quality as the main barrier According to the CAIA benchmark, even leading models achieve only 67.4% accuracy in high-stakes environments. Market Analysis Metric Value Organizations with Agent Observability 89% Quality Issues Rate 32% (main barrier) Evaluation Impact 60% reduction in production failures Target Customers: Companies deploying AI agents in production ...