Kapa for Agents is a product knowledge search solution for AI agents, released by Kapa.ai Inc. on June 10, 2026. It integrates and indexes information scattered across different locations and formats—such as documents, code, PDFs, customer support tickets, and Slack—into a single search layer, enabling existing agents to retrieve this knowledge via a Hosted MCP Server or Retrieval API. Rather than returning generic search result pages, it ranks and returns a small number of context chunks highly relevant to the query, providing citable search results so that agents can substantiate their answers. This approach is akin to a librarian selecting only the most relevant paragraphs and sources for a specific question, rather than forcing an agent to read every book in a library at once. Existing product knowledge agents typically relied on public web searches or required organizations to build their own Retrieval-Augmented Generation (RAG) pipelines, encompassing data collection, chunking, indexing, search, access control, and citation handling. Public web searches struggle to reliably distinguish between official documentation and informal posts, while maintaining self-built RAG systems becomes an operational burden as knowledge sources grow, requiring separate efforts for connector maintenance and search quality tuning. Kapa for Agents differentiates itself by offering this product knowledge search process as a standalone agent tool. Agent applications can focus on the consistent interface provided by the Hosted MCP Server or Retrieval API, rather than the storage mechanisms of each data source, allowing them to efficiently use limited context windows by receiving curated and ranked evidence instead of full original texts. Life sciences research organizations can utilize Kapa for Agents to build research support agents that connect experimental equipment manuals, analysis software documentation, internal Standard Operating Procedures (SOPs), code repositories, and support records. For example, if a researcher queries about a specific analysis error, the agent can retrieve relevant manual sections, code explanations, and existing support tickets from Kapa’s search results to generate a solution procedure with connected evidence. Additionally, teams operating bioinformatics platforms can integrate API documentation, example code, release notes, and technical discussions from Slack to supply developer support agents. In laboratory automation environments, it is appropriate to pass chunks retrieved from equipment PDFs and operational documents to task planning agents, while implementing researcher approval and original text verification steps before actual equipment control. However, the publicly available Discovery information does not disclose synchronization cycles per supported connector, chunking and reranking methods, access control granularity, data retention policies, deployment regions, or audit capabilities required for life sciences regulatory environments. Therefore, before connecting patient information or clinical data, it is necessary to separately review security documentation, personal data processing conditions, permission inheritance mechanisms, and regulatory compliance scope. Official GitHub repositories, specific SDKs, pricing tiers, throughput limits, and version information also require further verification.

Kapa for Agents is a product knowledge search solution for AI agents, released by Kapa.ai Inc. on June 10, 2026. It integrates and indexes information scattered across different locations and formats—such as documents, code, PDFs, customer support tickets, and Slack—into a single search layer, enabling existing agents to retrieve this knowledge via a Hosted MCP Server or Retrieval API. Instead of returning standard search result pages, it ranks and returns a small number of highly relevant context chunks for the query, allowing agents to

🔬 Automated Technical Support for Analysis Software: Index official documentation, Git repositories, and support tickets for analysis tools, and connect to the support agent via a Hosted MCP Server. The number of chunks returned per query, re-ranking criteria, and quantitative metrics for response latency require verification against official documentation; results must retain original citations so researchers can verify resolution procedures.