March 2026 Summaries

Agent harnesses are crucial tools in enterprise AI, designed to enhance the performance of AI agents by providing additional functionalities beyond what traditional agent frameworks offer. These harnesses can vary in their utility depending on the specific needs of the agent, whether it is a standalone product or part of a larger suite of agents. Common features of agent harnesses include preset prompts, toolsets, error handling, and advanced options like observability and multi-agent orchestration. They are categorized into general-purpose and vertical-specific harnesses, each serving distinct purposes; the former offers broad applicability across domains, while the latter is tailored to specific verticals like software development. Notable examples include the Claude Agent SDK, which provides robust infrastructure and integration capabilities, and DeepAgents, which emphasizes model-agnostic capabilities. Platforms like Credal offer solutions that allow for both general-purpose and vertical-specific tuning of agent harnesses, facilitating integration, orchestration, and context management to optimize AI operations in various business environments.

The Model Context Protocol (MCP) project has adopted the MCP Bundle Format (MCPB), which allows developers to package and share local MCP servers easily, similar to Chrome or VSCode extensions, for use in AI chat applications. While MCP Bundles facilitate easier integration by lowering adoption barriers and potentially expanding third-party connector ecosystems, they do not address security concerns inherent in integrating business tools with AI applications. The format requires a manifest.json file within a ZIP archive, outlining necessary server details, and was initially developed by Anthropic before being transferred to the open-source MCP project. Despite their convenience, MCP Bundles pose risks of shadow IT, as they do not include authentication or audit capabilities, leaving organizations vulnerable to ungoverned connections and potential supply chain threats. It is recommended that businesses use a governance layer like Credal to enforce security policies, audit usage, and manage permissions to mitigate these risks.

A financial planning team at a Fortune 50 company faced challenges using AI to efficiently and accurately answer specific questions from extensive Excel spreadsheets due to the limitations of AI models in handling large and messy data. Models like GPT 5.* often struggle with the sheer size and complexity of real-world spreadsheets, resulting in data truncation or inefficient resource use. To address this, Credal developed ReadSpreadsheet, an action specifically designed to enhance AI's capability to process large Excel files by intelligently identifying and compressing relevant data. Testing across multiple GPT generations showed significant improvements in accuracy and performance when ReadSpreadsheet was employed, particularly with older models. This tool effectively manages spreadsheet-specific challenges, ensuring that AI agents perform reliably and efficiently in real-world scenarios, thus facilitating better decision-making in data-heavy environments.

The blog post by Ravin Thambapillai discusses MCP transports, specifically focusing on the two built-in modes: Standard Input/Output (STDIO) and Streamable HTTP, which offer different configurations and benefits for handling MCP transactions. STDIO provides a persistent, stateful connection similar to TCP, while Streamable HTTP operates in a stateless manner akin to HTTP, offering scalability and simplicity. The post also explores the option of building custom transports for specific needs, highlighting the importance of adhering to principles like proper error handling, resource management, and security measures such as authentication, authorization, and input sanitization to ensure robust and secure transport implementations. Additionally, it emphasizes security considerations to protect data and systems, including encryption, rate limiting, and network security practices, noting the deprecation of HTTP with Server-Sent Events in favor of Streamable HTTP for better compatibility with modern infrastructure standards.

Google's Agent2Agent (A2A) Protocol, launched with support from major companies like Atlassian and Salesforce, was designed to become a universal language for AI agents, focusing on explicit communication and task completion through a microservice-like architecture. Despite its advanced features, such as stateful communication and enterprise-grade security, A2A struggled with adoption due to the overlap with existing capabilities of the more widely adopted MCP protocol, which offered ease of use and quick integration with existing tools. The complexity and high implementation burden of A2A contrasted with MCP's low learning curve and rapid utility, leading enterprises to favor MCP despite its security vulnerabilities. Credal has addressed these security concerns by developing a secure MCP platform that integrates seamlessly with various services, offering role-based monitoring, rate limiting, and governance to manage agent interactions effectively, making it a trusted choice for enterprises seeking to leverage generative AI securely.