Latest news with #RichWaldron
Techday NZ
7 days ago
- Business
- Techday NZ
Tray.ai unveils Merlin Agent Builder 2.0 for enterprise AI scale
has released Merlin Agent Builder 2.0, offering enterprise teams a platform that delivers AI agents capable of executing tasks beyond merely answering questions. The new version of Merlin Agent Builder introduces features designed to address persistent challenges in AI agent deployment within enterprises. Industry data indicates that while a majority of enterprises are investing over $500,000 per year on AI agents, many face difficulties in scaling and deriving value from these solutions. Key obstacles highlighted include lack of complete data, session memory limitations, challenges with large language model (LLM) configuration, and rigid deployment options. Addressing deployment and adoption challenges Rich Waldron, Co-Founder and Chief Executive Officer at said: "Enterprise teams aren't short on ambition when it comes to AI agents - but they are short on results. This release clears the path from prototype to production by removing the blockers that stall adoption. We've built the only platform where enterprises can go from idea to working agent - fast - without compromising trust, flexibility or scale. That's how agent-led transformation actually happens." According to a significant gap exists between building and actual usage of AI agents in workplace settings. Agents that are not integrated with comprehensive and up-to-date knowledge often lose context, make unreliable decisions, and force users to repeat information, which undermines user trust and can lead to underutilisation. Meanwhile, IT and AI teams find it difficult to align LLMs with appropriate use cases, particularly where multiple agents operate in parallel, and encounter added complexity when deploying agents across various platforms. To address these issues, upgraded solution includes advancements in four key areas: integration of smart data sources for rapid knowledge preparation, built-in memory for maintaining context across sessions, multi-LLM support, and streamlined omnichannel deployment. Smart data sources and session memory The Merlin Agent Builder 2.0 offers a new smart data sources feature aimed at simplifying the connection and synchronisation of both structured and unstructured enterprise knowledge. Through a single interface, users can link data from sources like file uploads or Google Drive. This data is then automatically prepared and vectorised to ensure agents are informed with relevant and reliable information. Alistair Russell, Co-Founder and Chief Technology Officer of commented: "Merlin Agent Builder isn't a services wrapper. It's a fundamental part of our product and built for ease of use and scale. It handles chunking and embedding at the source, ensuring each data source is optimally segmented and vectorized so agents are grounded in high-signal, relevant context. That means fewer retrieval failures, more reliable decisions, and agents that reason and take action. It's how teams move fast - without trade-offs." Addressing another common shortcoming of AI agents - context loss between interactions - Merlin Agent Builder 2.0 incorporates built-in memory capabilities. The platform enables agents to recall previous sessions, track conversation history, and manage both short-term and long-term memory requirements automatically. This aims to reduce the need for custom solutions and enhances continuity in user exchanges, improving adoption rates. Flexible large language model support As organisations deploy multiple agents to handle diverse business processes, the ability to configure each agent with the most suitable LLM becomes increasingly important. Merlin Agent Builder 2.0 supports multiple LLM providers, including OpenAI, Gemini, Bedrock, and Azure. Teams can assign specific models to individual agents with tailored configurations, avoiding proprietary lock-in and supporting privacy-driven workflows where necessary. Unified deployment across channels The updated release allows teams to build an agent once and deploy it seamlessly across communication and application environments such as Slack, web applications, and APIs, or for autonomous operations. The delivery configuration is incorporated directly into the agent setup process, which eliminates the need for repeated setup and technical adjustments for different channels. With these updates, targets what it identifies as critical needs for enterprises: simplified data onboarding, session-aware agents, flexible modelling, and consistent deployment experiences. The company states that by providing these features in a unified platform, both IT and business teams are better positioned to transition from pilot projects to production-ready AI agents that are actively used by employees and customers alike.
Forbes
24-06-2025
- Business
- Forbes
New integration threads are needed to form the data fabric for AI.
New data integration threads are needed to weave a stronger data fabric for AI use cases. Data demands direction. Left on its own, any information resource is of comparatively little value in and of itself until it is applied to a specific use case, inside a specific application (or other data service) through the specified calculations of an algorithm and under the auspices of a specific set of controls for provisioning, maintenance and management. Nowhere is this simple enough truth more evident than in AI. The technology industry loves to remind us that in AI, it's 'garbage in, garbage out' and that we should focus on the provenance and preparation of data long before we start to think about giving it a seat at the dinner (or boardroom) table and putting it to work. Sloppy Data In AI While the average user might expect data injection, ingestion and integration within the bedrock of AI to be a precise science, instances of AI can experience limitations as a result of poorly aligned data channels. It's not so much a case of garbage in or out, more a case of trying to work with good ingredients, but while working from the garbage heap… with refuse getting the way and a lack of clean work surfaces. In data wrangling terms, that translates to AI being built on incomplete knowledge bases, AI being deployed through public cloud computing resources or on-premises equipment that has memory limitations, the use of misaligned large language models and a lack of fluid 'information transports' exacerbated by rigid interaction channels. This is the pain point that AI integration and automation platform company Tray seeks to remedy. Rich Waldron, co-founder and CEO of Tray says that his firm's Merlin Agent Builder addresses the gap between agent deployment and real-world user adoption. Something like a sat-nav system designed to make sure AI agents not only get built, but actually get used and driven, he says that (all too often) agent experiences feel clunky and disconnected. 'This is because agents lose context, rely on narrow knowledge and force users to start from scratch in every session. Behind the scenes, IT and AI teams struggle to align the right LLMs to the right use cases, especially in multi-agent environments. Without flexible deployment options, it's hard to meet users where they work,' said Waldon 'To bridge the adoption gap, agents need smarter data access, built-in memory, LLM flexibility and tailored user interactions… and all that needs to be built to drive sustained usage, not just prototypes and demos.' Not A Services Wrapper Waldron's fellow co-founder and company CTO Alistair Russell insists that, 'Merlin Agent Builder isn't a services wrapper. It's a fundamental part of our product and is built for ease of use and scale.' By which he means that this product doesn't do what a service wrapper technology does i.e. act as an intermediary abstraction layer that enables non-native computing services to run on an operating system that they were not specifically designed for. Although service wrappers are popular for a variety of use cases (they extend functionality, enable management options and can provide granular control), Russell is suggesting that his firm's tools go deeper and work at the lower substrate layer, weaving a set of interconnections that exist far closer to where data itself is born. 'Our platform handles chunking [diving data into more manageable, digestible pieces] and embedding at the source, ensuring each data source is optimally segmented and vectorized so agents are grounded in high-signal, relevant context. That means fewer retrieval failures, more reliable decisions and agents that reason and take action," explained Russell. He asks us to imagine building an IT help desk agent to automate ticket resolution. But the knowledge it needs (past tickets, solution articles, internal policies etc.) is scattered across siloed systems. When agents can't find the right data for the user, user trust breaks down. The answers offered by agentic AI at this level start to 'feel a bit off' and conversations fall flat. 'For the teams building agents, one of the most time-consuming, technically challenging parts is grounding the agent in the right data. They rely on custom ingestion pipelines or manual preprocessing, burning developer time just to prepare knowledge for agent use. Even then, keeping that data updated and consistently accessible across agents is a challenge,' said Russell. 'Tray's data sources [controls] eliminate the barrier on both ends by making it easy for users to connect and sync structured and unstructured knowledge from file uploads or sources like Google Drive.' Today he laments, we are at a point where an IT help desk agent is answering follow-ups and handling escalations, but every time a user returns, it forgets the context of their earlier issue. This is because most agents forget everything between conversations. Short-term context often gets lost in other platforms due to token limits and storage constraints. Long-term memory usually requires custom engineering or patchwork workarounds to avoid frustrated users and disconnected experiences. To address this, the Tray team says that Merlin Agent Builder now includes maximum short- and long-term memory, so agents can track session history and refer back to prior conversations automatically. Competitive Analysis, iPaaS Integration For all Tray's data management, data wrangling, data channeling and data integration capabilities, it's not unreasonable to call the firm an iPaaS player. Analyst house 2025 Gartner places the company in its Magic Quadrant for iPaaS after all. The integration Platform-as-a-Service market is both variagated and various in the types of firms that dominate and proliferate in this space. Top usual suspects in this arena include Boomi, MuleSoft, Workato, SnapLogic, Jitterbit, Informatica and database giant Oracle with its own Oracle Integration Cloud technology. AWS, Microsoft, IBM and Huawei Cloud also all feature in this market. Workato is known for AI-fuelled automation technologies, some of which cover the integration space and many of which fall into the low-code tooling zone. More directly recognized as an integration purist, Boomi (for just over a decade in the 2010s part of Dell, but no longer) offers iPaaS capabilities that run across cloud-native, hybrid mixed environment and legacy data sources. Possibly the 'Windows of the iPaaS market', Boomi is thought of as user-friendly, but without the more advanced application programming interface connectivity that some vendors boast in this arena. Absolutely API-first is MuleSoft (part of Salesforce these days) with its MuleSoft Anypoint Platform, the company is all about API-centricity, API integration, API management and (logically enough these days) API AI. Lesser-known integration brands Jitterbit and Zapier enjoy adoption with smaller to medium-sized businesses that need rather more point-and-click technology services. SnapLogic wins some over for its speedy deployment, its big data alignment and the general appeal of its Iris AI service; commensurately, it loses some prospects over what some feel is its less transparent pricing structure. Data integration is deep and complex, so customer use case contracts shouldn't be, but they sometimes are. Vendors like Celigo get their niche status by offering data itgeration for more defined uses (in this case e-commerce), so think extra governance and security here. IBM App Connect, Microsoft Azure Logic Apps and Tibco Cloud Integration also all make up the smorgasbord of specialists in this market sector. As the iPaaS market continues to develop, AI will (perhaps obviously) feature more prominently as a purchasing decision factor. This will likely enable more iPaaS technologies to move outwards from the datacenter and work at the smart edge in the internet of things, all of which will see more real-time data streaming come to the fore as a critical must-have. All That Agent Talk Watch any news feed on agentic AI services and there are countless pages of new developments telling us about sparkling new agentic functions. Some will be applied to new HR use cases, some will work in disconnected air-gapped deployments such as military-grade software installations, some will offer point-and-click simplicity and some will offer new strains of cloud-native functionality so that they are well-suited to align with Kubernetes orchestration layers and so on. There is an almost infinite variety. What will make fewer headlines are the agentic functions that offer delivery data configuration and integration advancements for smart routing and the ability to maintain continuity across most complex multi-turn interactions, but that's what's happening here. This is a question of data direction for agents so that they steer us the right way and keep us out of the garbage heap.
Hamilton Spectator
21-05-2025
- Business
- Hamilton Spectator
Tray.ai Named iPaaS Leader Validating its Role as the Enterprise Platform for Composable AI and Agentic Automation
SAN FRANCISCO, May 21, 2025 (GLOBE NEWSWIRE) — the platform for building smart, secure AI agents at scale, has been named a Leader in the 2025 iPaaS Technology Value Matrix by Nucleus Research. This marks the sixth consecutive year has earned the Leader distinction, recognizing its continued innovation in composability, enterprise governance and a platform built for AI readiness and agent deployment. 'Every AI project is an integration project. That makes iPaaS the critical foundation for deploying agents,' said Rich Waldron, CEO and co-founder of 'Enterprises need one platform to connect systems, apply strong guardrails and power agents that take real action. That's where we focus.' From prototype to production: AI agents that act Merlin Agent Builder gives teams a visual, enterprise-ready way to build AI agents using natural language, reusable tools and built-in guardrails. With Agent Accelerators—pre-built, customizable templates—teams can quickly launch agents for common use cases like ITSM, knowledge management and customer support. Agents can be deployed in Slack, Teams or directly into workflows, and reused across departments with minimal setup. Agent capabilities include: According to Alexander Wurm, Senior Analyst at Nucleus Research: 'With a series of AI innovations and the introduction of Merlin Agent Builder and Agent Accelerators, Tray provides the leading platform for no-code agent development.' A composable foundation for integration, automation, and agent development With the Tray Universal Automation Cloud, enterprises can quickly create AI agents, integrate data, deploy APIs and orchestrate AI-first business processes — all in one place. These capabilities support broader AI, automation and integration goals so that teams can move faster, reduce complexity and scale with confidence. Key AI composability features include: Built-in governance to scale AI with confidence The Tray Universal Automation Cloud's Enterprise Core ensures centralized governance, instrumentation, security and scalability across every agent, integration or automation initiative for complete control. Governance is built into the core of the platform, not treated as an afterthought. These controls ensure that agentic automation can scale securely without compromising visibility, trust or compliance. Key AI governance features include: As enterprises scale their AI initiatives, Tray provides the foundation to move from proof of concept to production in a fast, flexible and safe way. To learn more: About offers a composable AI integration and automation platform that enterprises use to build smart, secure AI agents at scale. It eliminates the need for disparate tools and technologies to integrate and automate sophisticated internal and external business processes and speeds the creation and deployment of high-value, production-ready AI agents. Enterprises can now avoid the traps of high costs and long lead times typical in custom agent development as well as the constraints and silos created by implementing and managing single-purpose agent offers from each SaaS application in the enterprise tech stack. With the development of integrations, the delivery of intelligent apps and the integration of trusted data anywhere is fast, flexible and safe. Learn more at Media Contact: trayaiPR@
