List of the Top Agentic AI Platforms for Model Context Protocol (MCP) in 2026 - Page 4

pay.sh is designed as a pay-per-use API access platform for agents and command lines, allowing for effortless payments through a single command for any API. This innovation simplifies the experience for agents accessing paid APIs, enabling them to find services, analyze pricing, request access, and receive replies—all without the need for registration, account setup, or subscriptions. Specifically created for the agentic economy, pay.sh tackles the issues associated with conventional services that require human involvement in tasks like account creation, plan selections, API key generation, and credit card setups. By filling this void, pay.sh provides direct access to API calls that agents can conveniently discover, evaluate, and employ. Furthermore, it boasts an extensive directory that assists agents, developers, and API teams, allowing API providers to showcase their services in a user-friendly manner without the need for prior account verification. Agents can easily navigate the catalog, inspect endpoints, and utilize pay-per-use options across a diverse range of fields, including AI/ML, maps, data, search, messaging, compute, storage, and crypto/finance, ultimately boosting their operational efficiency and adaptability. By streamlining the entire API interaction process, pay.sh is fundamentally transforming how agents engage with APIs, making it a game-changer in the field. With its user-focused approach, it envisions a future where accessing API services is as straightforward as possible.

Agent Control is an innovative open-source framework that revolutionizes the management of AI agent behavior on a grand scale, establishing a new standard for governance in the field. It tackles the challenges posed by fragmented and hardcoded checks by equipping teams with a cohesive governance layer that applies regulations at every stage, all managed from a single control interface that can be dynamically updated without needing modifications to the agent's core code. Developers can easily identify any function for governance by using the control() decorator, turning critical decision points within an agent into separately regulated control points, each with tailored governance policies. When a function marked with this decorator is executed, Agent Control evaluates the input or output based on the relevant policy, generating responses that can include denying, steering, warning, logging, or allowing the action. Should a denial occur, the SDK raises a ControlViolationError, effectively blocking any potentially harmful actions from being carried out. This clear demarcation of policies from the actual code empowers developers to strategically position control hooks, while governance teams can focus on the specifics of enforcement, promoting a collaborative governance model. The adaptability and strength of Agent Control render it an essential resource for organizations aiming for effective standardization in AI agent governance, and its user-friendly interface further enhances accessibility for developers across various levels of expertise.

Preloop is an open-source control plane tailored for AI agents that can execute real-world tasks, featuring a robust multi-layered security system. This includes an MCP firewall for tool access management, an AI model gateway that promotes cost efficiency, safety, and accountability, along with policy-as-code that emphasizes human oversight, all while ensuring runtime session visibility and maintaining audit trails in a self-hosted environment. As AI agents rapidly gain the ability to deploy code, alter infrastructure, manage financial transactions, access production data, and generate model costs nearly instantaneously, Preloop equips teams with the tools to oversee agent activities, track spending, and identify which actions require human approval. It supports an array of tools such as OpenClaw, Hermes, Claude Code, Codex CLI, Cursor, Gemini CLI, Windsurf, Cline, OpenCode, and any agents compliant with MCP standards. Moreover, access rules can assess not just tool names but also their arguments and context, utilizing CEL expressions to set specific conditions. Teams are also given the option to start with observability features and gradually implement approval and denial processes without needing SDKs or significant changes to current applications, facilitating a more efficient rollout. This comprehensive strategy not only ensures that organizations retain control over the functionalities of their AI agents but also allows them to adapt to evolving needs and challenges in the AI landscape. Such flexibility is crucial in a rapidly changing technological environment where the implications of AI actions can be profound.

Nagent AI serves as an all-encompassing platform for enterprises, facilitating the evolution of teams from simply developing AI tools to launching intelligent applications capable of autonomously managing real business processes. With Nagent, a diverse range of professionals, including product managers and customer success representatives, can easily create, implement, and manage AI agents that possess the ability to learn, retain information, and execute tasks. The user-friendly no-code Agent Builder Studio combines models, tools, logic, knowledge, memory, and multimodal capabilities within a unified interface, allowing users to build agents from scratch, modify pre-existing templates, or receive support from an AI assistant to aid in various workflow components. Additionally, it features a distinctive multi-agentic workflow system that integrates multiple agents into a unified process, effortlessly connecting essential functions such as content generation, research, and reporting within an enterprise. Nagent supports over 40 AI models on a single platform, enabling users to take advantage of a range of models without the complications of managing multiple keys, accounts, or billing systems. This efficient approach not only boosts productivity but also enables teams to concentrate more on strategic endeavors rather than getting bogged down by technical challenges. Ultimately, Nagent AI transforms the way businesses leverage artificial intelligence, paving the way for innovative applications that drive success.

Whim is an innovative cloud-based development workspace specifically designed for the rapid and efficient deployment of AI coding agents. It empowers developers to utilize AI coding agents like Claude Code and Codex within secure cloud containers, eliminating the dependence on local hardware. Each task is assigned its own sandboxed Ubuntu environment, which includes full shell access, isolated git branches, and real-time terminal streaming. This infrastructure seamlessly integrates AI coding agents into developers' daily workflows, fostering collaboration and parallelism while removing the complexities of local setup. Users can effortlessly connect a repository, create a prompt, and the AI agent will initiate its work within a secure cloud container that can be accessed from any device. Moreover, multiple tasks can run simultaneously, allowing for experimentation with different approaches, focusing on various features, or enabling an orchestrator to manage a team of agents without causing disruptions. Whim is also compatible with native CLI runtimes for Claude and GPT models, with plans to expand its offerings through OpenRouter in the near future. This adaptability makes Whim a valuable asset for boosting productivity in software development contexts, while also paving the way for future enhancements and integrations that could further improve the user experience.

MemClaw functions as a robust memory service designed specifically for LLM-driven agents, acting as a structured shared memory layer for groups of agents. Its primary objective is to promote collaborative learning among AI agents by merging their individual contexts into a unified Company Brain, which features built-in memory capabilities, governance, provenance tracking, contradiction detection, and established visibility scopes from the very beginning. The architecture of MemClaw clearly separates an organization’s agents—including tenants, fleets, nodes, and individual agents—from the managed memory layer through elements such as the MCP Server, REST API, OpenClaw plugin, MemClaw Core, and durable storage solutions. Agents can seamlessly access and contribute to the Company Brain via MCP-compatible tools, direct HTTPS requests, or integrations through OpenClaw. Meanwhile, the MemClaw Core enhances data management by executing functions like entity extraction, contradiction detection, PII screening, and lifecycle management before any information is committed to storage. Each memory entry can be tagged with a specific visibility scope and sorted into various categories such as fact, episode, decision, preference, rule, plan, commitment, action, and outcome. This organized method not only improves the classification of information but significantly boosts the overall efficiency and efficacy of interactions among AI agents within the network. Ultimately, the cohesive framework provided by MemClaw ensures that agents can work together more intelligently and purposefully.

Dock is an innovative collaborative AI workspace tailored for you, your team, and the diverse agents you utilize. It provides a cohesive cloud environment where both human users and AI agents can simultaneously access and update information in real-time, eliminating the hassle of scattered chats, files, and disconnected outputs. The platform is organized around structured tables with specific columns, rich-text documents, and treats agents as central entities, each with their own API keys, permissions, and audit trails, thereby removing the necessity for human-delegated tokens. Teams can harness Dock for a wide range of activities, such as planning, researching, making decisions, and executing projects, all within a collective interface that supports contributions from both humans and AI. The versatility of Dock allows for applications in various fields, including engineering, go-to-market strategies, research, operations, individual projects, and agency tasks. Engineering groups can take advantage of Dock to enhance sprint planning, generate specification documents, and respond adeptly to incidents; marketing departments can optimize content calendars, oversee sales pipelines, and elevate customer success strategies; research teams can systematically document interviews, extract key themes, and analyze competitive intelligence; and operations teams can manage runbooks, streamline recruitment processes, ensure compliance, and coordinate onboarding initiatives. By creating this integrated environment, Dock not only boosts productivity but also drives innovation across all areas of team operations, ultimately leading to more effective collaboration. In conclusion, Dock is a transformative tool that redefines how teams work together in an increasingly digital landscape.

Tulsk is an innovative project management platform tailored for small startup teams, streamlining the organization, execution, and monitoring of independent AI tasks across diverse components such as projects, documents, assignments, comments, and agent workflows within a single interface. By adopting Tulsk, teams can allocate real tasks to AI agents instead of navigating through multiple chat interfaces with fragmented outputs and prompts. Users can conveniently tag an agent in any task, enabling it to understand the context, carry out the required tasks, and provide results directly in the conversation without the hassle of copy-pasting or oversight. This comprehensive workspace brings together projects, statuses, priorities, attachments, live commentary, the OpenClaw agent runtime, an EMA AI project manager, Skills, MCP access, and functionalities for scheduling agents. The OpenClaw environment offers agents their dedicated cloud space that includes a browser, shell, web search features, modifiable persona files, pertinent skills, and various tools, which empowers them to efficiently handle extensive responsibilities such as market analysis, competitor evaluations, report creation, content generation, operational assessments, and customized workflows. Moreover, Tulsk not only fosters better collaboration among team members but also considerably reduces the workload, allowing teams to channel more energy into driving strategic growth and fostering innovation, ultimately setting a foundation for long-term success.

The Agent Client Protocol (ACP) is designed to streamline communication between code editors, integrated development environments (IDEs), and coding agents, promoting a standard for agent-editor interoperability instead of requiring distinct integrations for each possible combination. It creates a universal interface for AI agents to interact with client applications, featuring a robust, adaptable, and platform-agnostic framework that accommodates both local and remote scenarios. By addressing challenges related to integration expenses, restricted compatibility, and reliance on developers, ACP enables agents that comply with the protocol to operate effortlessly with any compatible editor. Simultaneously, editors that adopt ACP gain access to a broader array of ACP-compliant agents. Similar to how the Language Server Protocol enabled standardized integration of language servers, ACP decouples agents from editors, allowing both entities to progress autonomously; this flexibility empowers developers to choose the best tools tailored to their unique workflows. Ultimately, this advancement cultivates a cooperative atmosphere where tools can be easily integrated, significantly boosting developers' overall productivity and efficiency while creating opportunities for innovation in software development.

OpenViking serves as an innovative open-source context database specifically designed for AI agents, employing a file-system-based architecture to optimize the organization of memories, resources, and skills. Instead of treating context as scattered elements within a fragmented vector store, OpenViking integrates agent context into a cohesive virtual file system via the viking protocol, which empowers agents to efficiently store, explore, retrieve, and observe essential information. This framework significantly reduces the challenges associated with manual context management for developers, providing a simplified interaction model reminiscent of traditional file operations. Additionally, OpenViking supports hierarchical context loading, enabling semantic and recursive data retrieval, effective session management, comprehensive metrics tracking, and enhanced observability. As a result, AI agents can efficiently access relevant information without being inundated by excessive prompts. Ultimately, by implementing this advanced system, developers can substantially improve the overall performance and capability of their AI solutions.

Hindsight represents a groundbreaking memory architecture aimed at improving AI agents by allowing them to learn incrementally instead of erasing their knowledge after each interaction. In contrast to conventional memory systems that mainly concentrate on retrieving past dialogues, Hindsight emphasizes the learning journey, providing agents with a robust long-term memory supported by sophisticated biomimetic data structures. This approach enables AI agents to monitor critical information, retrieve pertinent context, and engage in reflective reasoning informed by their prior experiences. Particularly advantageous for agents needing comprehensive awareness of user identities, past conversations, shifting preferences, decision-making patterns, and essential behavioral adjustments across various sessions, Hindsight offers a significant advantage. To facilitate this, it integrates three core operations: retain, which captures new insights; recall, which retrieves relevant memories as needed; and reflect, which assists agents in synthesizing observations, constructing mental models, and deriving valuable insights from past interactions. By incorporating these functionalities, Hindsight not only fosters a more tailored and contextually aware user experience but also promotes ongoing development and adaptation of the AI agents over time. Ultimately, this innovative framework marks a significant advancement in the evolution of intelligent systems.

claude-mem functions as an offline-first cloud memory solution designed for AI agents, built around an open-source engine paired with a cloud synchronization layer that universally connects agent memories via a single private MCP link. Its architecture guarantees that coding agents and AI assistants can seamlessly continue their work without starting anew in each session, independent of the machine or code editor utilized. As agents operate, claude-mem adeptly captures notes that reflect decisions, solutions, challenges, environmental insights, architectural selections, and various structured observations within a temporal database. The CMEM Cloud subsequently replicates this local memory using a private Model Context Protocol endpoint, allowing any compatible agent or integrated development environment to access and modify shared memory across multiple platforms, including Claude Code, Cursor, Windsurf, OpenCode, Codex CLI, Gemini CLI, and VS Code. Primarily functioning in a local environment, it retains its capabilities regardless of network availability, ensuring that memory synchronization occurs whenever cloud access is established. This cutting-edge methodology significantly enhances the continuity of AI interactions, thereby providing a more cohesive experience for both developers and users, ultimately leading to more efficient workflows.

CMEM Cloud functions as the connective synchronization layer for claude-mem, facilitating universal memory access for AI agents through a private MCP link. The open-source framework of claude-mem captures notes while agents execute tasks, and CMEM Cloud mirrors this local memory, granting agents the ability to retrieve it smoothly across various sessions, devices, editors, and MCP-compatible clients. This cutting-edge system removes the necessity for users to constantly reiterate context, transfer previous notes, or begin anew, as it automatically logs key decisions, bug fixes, dead ends, environmental observations, architectural choices, and other structured insights in real-time. These important insights are stored in a temporal database, enabling searches based on meaning through vector recall, and can be accessed via a private MCP endpoint that any compatible agent can use for reading and writing purposes. The process begins with the setup of the local engine, followed by the activation of a secondary model that autonomously generates structured notes, syncing the local database with CMEM Cloud, and ultimately allowing for memory recall from any location. This method not only boosts efficiency but also cultivates a more collaborative atmosphere among agents, as they can share insights with ease and contribute to a more cohesive working environment. As a result, agents can work more effectively together, leveraging shared knowledge to enhance their collective performance.

Ejentum acts as a systematic reasoning framework designed specifically for agentic AI, improving the trustworthiness, traceability, and consistency of LLM agents when handling complex or lengthy assignments. This groundbreaking tool can be activated by agents during ongoing tasks, allowing for accurate cognitive processes tailored to their unique challenges, which provides opportunities for immediate adjustments in reasoning rather than relying solely on fixed prompts. Created to prevent AI agents from wandering off track, flattering users, creating falsehoods, or clinging to erroneous assumptions, Ejentum also guarantees that they do not accept shallow answers or lose crucial context through successive interactions. The framework features an impressive 679 capabilities categorized into four cognitive harnesses: reasoning, code, anti-deception, and memory. The reasoning harness specifically focuses analytical capabilities on comprehending causality, temporal aspects, spatial relationships, simulations, abstractions, and metacognition, thereby helping agents avoid superficial pattern recognition. Through the integration of these varied functionalities, Ejentum empowers AI to engage more profoundly with tasks, leading to improvements in the overall quality and depth of their outputs. Moreover, this structured approach not only enhances operational effectiveness but also fosters a more profound understanding of intricate problem-solving scenarios.

Manufact is an all-encompassing platform tailored for the development and deployment of MCP applications and servers, granting teams quick access to resources like the ChatGPT Apps Store, Claude Connectors, and multiple user-agent interaction channels. The mcp-use SDK serves as an integrated MCP framework that aids in crafting MCP applications compatible with both ChatGPT and Claude, in addition to creating specific MCP servers designed for AI agents. With Manufact, each stage of the MCP lifecycle is optimized, eliminating the requirement for extra tools: developers can create using an SDK, a skill, or a vibe; begin deployment with a simple command; publish by adhering to marketplace standards and taking advantage of auto-generated submission materials; enhance their creations through Cloud Inspector; and monitor performance through features such as analytics, session replays, trace logs, error metrics, and alerts. Teams enjoy the adaptability to scaffold using the MCP-use SDK, embed a skill within a coding agent, draft an app while witnessing the scaffolding process, or effortlessly integrate an existing MCP server without needing alterations. Furthermore, Manufact Cloud allows for a one-time connection to a repository, ensuring that every push results in automatic deployment and providing preview URLs for pull requests, along with the administration of custom domain configurations and SSL certificates. This comprehensive solution empowers teams to prioritize innovation over the intricacies of managing infrastructure, paving the way for creative breakthroughs and efficient workflows. In this way, Manufact not only simplifies the development process but also fosters a collaborative environment where ideas can flourish.

Sendmux offers specialized inboxes for AI agents along with an efficient framework for email communication. Each agent can handle incoming messages, assess conversation threads, organize attachments, respond to emails, and interact with new mail via signed webhooks or Server-Sent Events. Teams have the option to send emails using various methods, including Gmail OAuth, Outlook OAuth, SMTP services, or managed Amazon SES, providing them with flexibility in their email operations. Furthermore, Sendmux allows for the distribution of email sending across multiple providers through features like weighted routing, sender-domain routing, health checks, failover strategies, and customizable quotas that can be set for different timeframes such as seconds, minutes, hours, or days. Developers benefit from a suite of tools including Mailbox, Sending, and Management APIs, extensive OpenAPI documentation, SDKs, the sendmux CLI, and MCP utilities, along with Sendmux Skills. Additionally, the system guarantees tenant isolation, offers mailbox-specific API keys, accommodates custom domains, and features robust logging and metrics, all while adhering to a usage-based pricing structure that avoids charging per seat or mailbox. This thorough design not only streamlines email management for teams and developers but also enhances their overall communication efficiency. By prioritizing flexibility and functionality, Sendmux positions itself as a pivotal tool in the landscape of email communication solutions.

Scalekit is a production-ready authentication platform that helps developers build AI agents capable of securely interacting with SaaS applications, APIs, databases, internal services, and MCP servers. Instead of giving agents broad organizational access through service accounts, Scalekit enables every action to be performed on behalf of an authenticated user using delegated identity, approved OAuth permissions, and tenant-specific authorization. The platform manages the complete authentication lifecycle, including OAuth flows, credential vaulting, secure token storage, automatic token refresh, authorization validation, and API execution, allowing developers to focus on application logic instead of identity infrastructure. Scalekit supports more than 100 prebuilt connectors while also allowing teams to create custom integrations for proprietary APIs, enterprise systems, and internal services. The platform automatically handles operational concerns such as pagination, retries, rate limiting, error handling, and credential resolution before returning clean responses to AI agents. Every interaction generates a detailed audit trail that records which user authorized the request, which agent executed it, what permissions were used, and what actions were performed, making compliance and security reviews significantly easier. Enterprise security features include AES-256 encrypted credential storage, per-tenant isolation, multi-region deployment, private cloud hosting, SIEM integration, and a 99.99% uptime SLA. Scalekit also provides authentication infrastructure for SaaS products, including SSO, SCIM provisioning, RBAC, passwordless authentication, and organization management, enabling teams to support both human users and AI agents from a single platform. Its architecture is designed for organizations deploying customer-facing and internal AI agents that require secure, delegated access across multiple business systems.

AgentKey provides a smooth integration of your AI agents with various external data sources through a single access key, allowing them to carry out actual tasks with efficiency. Although your agent may be knowledgeable enough to perform certain actions, it still needs the right APIs and services to carry them out effectively. With AgentKey, this entire procedure is simplified, enabling the agent to perform thorough searches, retrieve information from web pages, collect social insights, and integrate context from diverse fields such as finance, ecommerce, business, cryptocurrency, and on-chain data in one unified operation. This tool is specifically designed to function with platforms like Claude Code, Codex, Cursor, Windsurf, Gemini CLI, OpenClaw, Hermes, Antigravity, and Warp, along with any system compatible with MCP or Skills files, thus providing agents with the ability to tap into a vast pool of information without needing to juggle multiple provider dashboards. The search functionalities include services like Brave Search, Tavily, Serper, Perplexity, Parallel, and Exa, while the scraping capabilities utilize tools such as Firecrawl, Jina, and Bright Data to transform web content into actionable insights. By employing this cutting-edge solution, not only is operational efficiency significantly improved, but agents are also empowered to produce results that are more informed and enriched with context. Consequently, AgentKey represents a transformative leap in how AI agents can interact with and utilize data from diverse sources.

ZooData functions as a specialized data layer designed for agents, offering AI systems access to curated web content and actionable commercial insights through a singular integration point. Instead of delivering raw HTML or static reports meant for human consumption, it provides structured and token-efficient JSON, which agents can readily employ for research, analysis, monitoring, and automated decision-making processes. The platform has the capability to monitor over 500 million products across various marketplaces, including Amazon and TikTok Shop, and features more than 40 filters that aid in product discovery, batch processing, and in-depth opportunity exploration. Its data modules deliver an extensive analysis of market categories, insights into demand trends, metrics on competition density, margin benchmarks, and comprehensive competitor evaluations, in addition to real-time product signals such as pricing, inventory levels, and Best Sellers Rank. Furthermore, ZooData maintains a rich historical database with over two years of information on prices, sales, ratings, and rankings, which is crucial for performing time-series analysis and recognizing trends at an early stage. This extensive functionality makes ZooData an indispensable resource for businesses aiming to harness data for a competitive edge, ensuring they stay ahead in a rapidly evolving market landscape.

Maguyva represents a cutting-edge, agent-first code intelligence platform, equipping developers with AI-driven coding tools that generate a prioritized mapping of a repository before any changes are implemented. By seamlessly integrating with GitHub repositories, teams benefit from cloud pipelines that adeptly parse, rank, and index a variety of components such as symbols, dependencies, imports, semantic relationships, and cross-file structures, ensuring the index is continuously updated alongside the code's progression. With just one remote MCP integration, agents on platforms including Claude Code, Cursor, VS Code, Windsurf, Codex, and Gemini CLI can share a grounded context effortlessly, eliminating the need for a local indexer or modifications to their usual editing environments. The 11 MCP tools harness a blend of semantic, structural, graph, and text retrieval across five distinct search modalities, providing ranked results instead of mere raw grep outputs. Users are empowered to ask questions in natural language, locate essential symbols, recognize patterns through AST-aware searches, trail dependencies, identify orphaned code, assess the potential impact of modifications, and compile task context before engaging with any file, thereby amplifying productivity and collaboration among development teams. This efficient approach not only streamlines coding processes but also cultivates improved communication and teamwork, ultimately leading to a more cohesive development experience. Furthermore, by offering these advanced capabilities, Maguyva positions itself as a vital tool for modern software development.

Aura acts as an intelligent assistant for developers working with Unreal Engine and Unity, allowing them to define specific game features that can be integrated into their projects with ease. It meticulously analyzes the distinct context of each development effort, addressing technical questions, crafting implementation plans, and executing authorized changes through its Ask, Plan, and Agent capabilities. In the context of Unreal Engine, Aura can generate and modify a wide array of components, including Blueprints, enums, structs, data tables, C++ files, user interfaces, assets, levels, behavior trees, visual effects, audio elements, and other vital game components. Additionally, it is equipped to compile code, participate in Live Coding sessions, perform bulk editing tasks, locate assets, and leverage various documents such as images, design specifications, spreadsheets, PDFs, presentations, and more for contextual reference. Moreover, Aura is capable of developing new features and conducting playtesting, effectively linking the implementation and testing stages by evaluating gameplay experiences and user interfaces, diagnosing bugs, and automatically recovering from unexpected editor crashes during its operation. With its extensive capabilities, Aura stands out as a formidable resource for streamlining and enhancing the overall game development workflow. This multifaceted tool not only improves efficiency but also fosters creativity among developers by simplifying complex tasks.

Oqoqo is an all-encompassing platform designed for the development of evaluations and customized benchmarks for practical tasks requiring agency, allowing teams to carry out extensive experiments in realistic environments through fully managed cloud services. Users are empowered to create private collections of tasks and assessment criteria, evaluating agents on their interactions with a variety of products, including skills, MCP servers, CLIs, SDKs, APIs, documentation, and files, while also enabling comparisons among agents, models, interventions, and levels of effort under controlled conditions. Each task is executed within its own distinct environment, equipped with the essential project state, context, files, tools, and credentials needed for seamless operation. Oqoqo carefully tracks every detail of each execution, recording commands, tool interactions, errors, files, and the specific moment when an agent fails, ultimately yielding metrics such as pass or fail outcomes, pass rates, improvements, token usage, and friction points. These insightful metrics allow teams to identify problems within product interfaces, tackle token inefficiencies, scrutinize performance differences, correct failures, and re-run experiments for additional refinement and learning. This ongoing process cultivates an atmosphere of continuous enhancement, ensuring that agents are perpetually refined for maximum effectiveness, while also fostering collaboration and knowledge sharing among team members. Such an environment not only improves agent performance but also drives innovation within the team.

Archestra is an open-source, self-hosted AI platform that facilitates the management and deployment of agents across an organization. It boasts agentic chat capabilities designed for users without a technical background, in addition to various applications, skills, collaborative initiatives, a server-side agent runtime, MCP orchestration, permission-aware RAG, LLM and MCP proxies, security safeguards, and extensive observability, all seamlessly integrated into one platform. Through single sign-on (SSO) authentication, users can engage with tools while maintaining their unique identities rather than relying on shared service accounts. The platform organizes projects to bring together chats, files, scheduled tasks, and instructions, while agents function within isolated containers, activated by triggers such as schedules, emails, or webhooks. MCP servers are deployed within the organization's Kubernetes environment, adhering to security-validated promotion protocols that implement separate credentials and network rules. Additionally, the knowledge bases can connect with Confluence, Jira, drives, and internal documents, preserving source-system ACLs to ensure users only access information they are authorized to view. Overall, this extensive array of features establishes Archestra as an essential asset for organizations aiming to enhance their AI deployment efficiency and governance. By offering a user-friendly experience coupled with robust security measures, Archestra empowers teams to innovate while maintaining stringent access controls.

SearchUnify is an enterprise Agentic AI platform that connects fragmented enterprise knowledge and deploys autonomous AI agents to handle customer support operations from first contact to resolution. At its core, the platform is powered by SearchUnifyFRAG™ (Federated Retrieval Augmented Generation), Agentic RAG, and the SCORE (Contextual Relevance Engine) framework, enabling accurate, grounded responses drawn from unified enterprise data. The product suite spans five core tools. Cognitive Search retrieves contextually relevant answers across distributed knowledge sources. SearchUnifyGPT™ generates cited AI responses at every support touchpoint. SUVA, the virtual assistant, handles multi-turn conversations autonomously. Knowbler supports knowledge capture, structuring, and surfacing at the point of need. Agent Helper delivers next-best-action recommendations to support agents during live case resolution. Extending these capabilities, the Agentic AI Suite comprises eight AI agents: AI Support Agent for self-service resolution, AI SupportPlus Agent for autonomous CRM ticket handling, AI Classification Agent for case tagging and routing, AI Escalation Manager for escalation risk prediction, AI Knowledge Agent for automated content creation and gap detection, AI Competency Agent for L2 technical workflow resolution, AI Agent Partner for real-time agent guidance, and AI Case Quality Auditor for evaluating every closed case. SearchUnify supports BYOLLM for LLM flexibility, Model Context Protocol (MCP) for secure system connectivity, and a governance layer covering role-based access control, PII masking, and ISO 27001, HIPAA, and SOC 2 compliance. The platform connects with 100+ enterprise tools including Salesforce, Zendesk, and ServiceNow.

Coral is an open-source SQL query layer built to help AI agents and developers retrieve data from many systems without custom integration work. The platform connects to APIs, databases, and file systems, then exposes each source as a readonly schema that can be queried like a table. Teams can use Coral to combine information from tools such as GitHub, GitLab, Slack, Linear, Datadog, Sentry, OpenTelemetry, ClickUp, Incident.io, Intercom, Stripe, and PagerDuty. This makes it possible to answer complex operational questions with joins across engineering, communication, observability, workflow, and payment data. Coral is designed to work with both the CLI and MCP, allowing agents such as Claude Code or Codex to access one shared runtime. The platform manages authentication, pagination, rate limits, schema discovery, and source-specific execution details behind the scenes. Its readonly design helps agents gather context without mutating upstream systems or creating unnecessary safety risks. Coral also improves over time by learning schema hints, relationships, recommended joins, and query patterns from real usage. Features such as query pushdown, caching, and efficient pagination help reduce unnecessary API calls and lower token-heavy agent workflows. Teams can use Coral for coding assistance, AI SRE workflows, security and compliance investigations, customer escalations, and internal operations support. Coral helps organizations turn fragmented data sources into a unified query environment that makes agents more accurate, cost-efficient, and production-ready.