Deployed Engineer - Professional Services

DXC and LangChain Advance Agentic AI Harnesses for Mission-Critical IT Operations. Oct 13, 2026, 00:01 ET * Partnership brings together DXC's decades of experience operating mission-critical enterprise systems with LangChain's technology for safely and securely managing AI agent harnesses * DXC OASIS, built on LangChain's open-source LangGraph runtime, is scaling toward 125 customer environments across regulated industries and multiple geographies * Across 500 measured use cases, OASIS has reduced resolution time by 95% on average, with diagnostic accuracy of 92% or better ASHBURN, Va., Oct. 13, 2026 /PRNewswire/ - DXC Technology (NYSE: DXC), a leading enterprise technology and innovation partner, today announced a partnership with LangChain, the company behind the open-source LangGraph agent framework and LangSmith agent engineering platform, to advance governed agentic AI across mission-critical enterprise IT environments. The partnership builds on DXC's use of LangGraph within DXC OASIS, its intelligent orchestration platform and operating model for mission-critical IT operations. DXC is also licensing LangSmith to support the development, evaluation, and observability of OASIS agents. OASIS is already in production and scaling toward 125 customer environments across regulated industries and multiple geographies. Together, these capabilities help DXC build, orchestrate, evaluate, and continuously improve AI agents while applying the enterprise context, governance, human oversight, and continuous assurance required for critical technology environments. Bringing Agentic AI Into Mission-Critical Operations Through the partnership, DXC is using LangGraph and LangSmith to advance how OASIS agents are built, orchestrated, evaluated, and continuously improved. LangGraph provides the open-source framework for agent orchestration, including capabilities such as delegation, state management, and human-in-the-loop workflows, while LangSmith provides tooling for agent development, evaluation, and observability. OASIS integrates these capabilities to enterprise IT operations, enabling agents to respond to operational events, investigate issues in parallel, draw on specialized capabilities across technology domains, and bring people into the process when judgment or approval is required. This allows human experts to focus on higher-value decisions while agents provide the capacity and speed to address operational issues at scale. Across specific production use cases, filesystem incidents that previously took 53-90 minutes can be resolved in 3-6 minutes, while database performance incidents have moved from 2-3 hours to 5-12 minutes. Complex problem management that previously required 2-6 days has been reduced to 8-10 minutes. "Agentic AI has the potential to fundamentally change how enterprises operate their most critical technology, but models alone are not enough," said Dan Gray, President, Global Infrastructure Services, DXC Technology. "Across more than 500 measured use cases, OASIS has reduced resolution time by 95% on average, with diagnostic accuracy of 92% or better when validated against senior engineer judgment. Through our work with LangChain, we're bringing together advanced agent technology, operational expertise, and governance to put AI agents to work safely at enterprise scale." Scaling Agentic AI with Governance and Human Oversight A core focus of the partnership is helping enterprises expand the use of AI agents while maintaining appropriate governance and human oversight. Using LangChain technology, OASIS now enables agents to operate within defined authority while maintaining the controls required for mission-critical environments. Actions are assessed based on factors including reversibility, impact, verifiability, and an agent's track record. Sensitive actions can be paused for human approval before execution, while agent decisions, tool calls, and outcomes are traced to provide visibility into how agents operate. OASIS also grounds agents in the specific enterprise environments in which they operate. Agents can draw from system-specific and customer-specific information, DXC operational expertise, and vendor documentation, helping them reason using the context and requirements of each customer environment rather than relying solely on general model knowledge. By combining this enterprise context with defined governance policies and human oversight, OASIS enables organizations to expand the role of AI agents in production while maintaining accountability and control. "Agents are transforming the application layer for every business, and the enterprises pulling ahead are taking ownership of their agent stack," said Julia Schottenstein, Chief Operating Officer, LangChain. "That means adapting the harness to their business, grounding agents in proprietary context, and building the governance and continuous improvement loops that make agents reliable in production. We're proud to bring that model into mission-critical enterprise operations through our partnership with DXC." An Open Approach to Enterprise Agentic AI The partnership also supports an open approach to agentic AI designed to give enterprises flexibility and control as AI technologies evolve. OASIS is model agnostic and supports interoperability with third-party and customer-owned systems, enabling organizations to select different models for different workloads based on requirements including quality, cost, latency, and privacy. OASIS is also designed to continuously improve as it operates, using insights from agent executions to improve performance and incorporate lessons from individual scenarios into the broader system over time. Deployments can span public cloud, private cloud, dedicated, and sovereign environments without requiring a redesign of the underlying architecture. Customers retain control of their data, workflows, and the operational capabilities they build on top of the platform, while preserving flexibility across models, infrastructure, and deployment environments. DXC and LangChain detail their approach in a new technical white paper, DXC OASIS: The Harness for Mission-Critical IT Operations, which explores how organizations can operationalize AI agents while maintaining governance, accountability, and human oversight across mission-critical environments. About DXC Technology DXC Technology (NYSE: DXC) is a leading enterprise technology and innovation partner delivering software, services, and solutions to global enterprises and public sector organizations - helping them harness AI to drive outcomes at a time of exponential change with speed. With deep expertise in Managed Infrastructure Services, Application Modernization, and Industry-Specific Software Solutions, DXC modernizes, secures, and operates some of the world's most complex technology estates. Learn more on dxc.com.

DXC Technology has partnered with LangChain to bring governed AI agents into mission-critical IT operations. DXC uses LangChain's LangGraph framework within its OASIS orchestration platform, and is licensing the LangSmith platform to develop and evaluate OASIS agents. OASIS is in production and scaling toward 125 customer environments across regulated industries. Across more than 500 measured use cases, DXC says it has cut resolution time by 95% on average, with diagnostic accuracy of 92% or better. Database performance incidents that took two to three hours now take 5-12 minutes. Agents act within defined authority, and sensitive actions can be paused for human approval. Their decisions and tool calls are traced. OASIS is model agnostic and can run on public cloud, private cloud, dedicated, and sovereign environments.

Querit Search API launches Code Search for AI Coding Agents. Sep 30, 2026, 11:00 ET SINGAPORE, Sept. 30, 2026 /PRNewswire/ - Querit, a search infrastructure company purpose-built for AI, today announced the official launch of a dedicated Code Search capability for its core Querit Search API. By simply configuring a single parameter (Vertical=Code), developers can instantly pivot from general web search to a programming-focused search optimized for code generation, IDEs, and deep engineering workflows. Rather than layering a simple filter on top of general web search, Querit treats vertical retrieval as a first-class capability, giving AI coding agents verifiable technical context aligned with programming constraints and helping reduce failures caused by stale, incorrect, or mismatched information. Querit Search API: A Real-Time Information Layer for AI Agents The Querit Search API is a real-time information layer built for AI agents. It runs on Querit's proprietary web index, which covers hundreds of billions of pages, spans multilingual and cross-regional coverage, and draws on authoritative sources from regulatory bodies and standards organizations to technical documentation and professional communities. Unlike traditional link-by-link browsing designed for humans, Querit is built for an agent's end goal of task delivery. Rather than returning only a list of links, it delivers structured, model-ready search results and content designed to support reasoning, grounding, and verification. On FreshQA, a public benchmark for time-sensitive retrieval, the Querit Search API ranked #1 with 83.17% accuracy. The Coding Vertical: Retrieval Rebuilt for Coding Agents Many coding-agent failures come not from high-level reasoning, but from real-world engineering constraints, framework and dependency versions, runtime environments, complexity requirements, and long-tail errors. General-purpose search may surface results that are semantically relevant yet incompatible with those constraints, increasing the risk of incorrect code or failed execution. Querit rebuilt its coding vertical along three axes: * High-quality source prioritization: prioritize official documentation, API references, SDK manuals, and trusted developer communities such as Stack Overflow, while reducing the weight of stale or low-quality secondary content. * Constraint-aware retrieval: specialized models identify programming languages, framework versions, runtime environments, complexity requirements, and error signatures, filtering out results that are semantically relevant but incompatible with the task. * Adaptive, structured output: granularity scales from a single API how-to to an end-to-end task, delivering high-density, clean text within a strict token budget while preserving code blocks and parameters. In internal, programming-focused evaluations, 81% of the results Querit Search API returned were directly adoptable by coding agents across core tasks such as function generation, autocomplete, and debugging - scored under identical token budgets by an LLM-as-a-judge for intent and constraint satisfaction. Asked to "reverse a string in JavaScript without built-in methods or extra data structures, within a set complexity," for example, Querit prioritizes implementations that satisfy every constraint, where control-group results routinely do not. Available Today, Completing the Real-Time Information Layer for AI "The value of an AI agent is in what it can deliver," said Shawn Xu, Head of Global Product at Querit. "General search decides what an agent sees; deep, vertical retrieval decides how good its output is. We're building a real-time information layer that truly understands task intent - and coding is where that architecture proves itself." The Querit Search API and its code vertical are generally available worldwide today. Existing Search API users can enable Code Search without changing endpoints or reworking their integration, simply by setting the code vertical parameter in the request. Beyond direct API access, Querit supports the Model Context Protocol (MCP) and is integrated with agent frameworks and platforms including LangChain, Dify, RAGFlow, CAMEL-AI, Cherry Studio, Eigent, EigenFlux, Continua, ModelOS, and Atlas Cloud, so developers can call it inside the toolchains they already use. Together with the Contents API and Monitors API, the Search API forms a real-time information layer for AI spanning web search, content extraction, and continuous monitoring. Next, coding coverage will expand to major open-source repositories. Read the docs and start a trial at www.querit.ai. About Querit Founded in 2025 and headquartered in Singapore, Querit is a search infrastructure company purpose-built for AI that empowers developers to build systems capable of efficiently searching, retrieving, and processing information from the Open Web. SOURCE Querit