agentic systems

Evidence (13)

• 5The New Stack·8/4/2026research
  Why AI Should Be Split Across the Software Delivery Lifecycle

  The article argues that AI in software development should be treated as an architectural layer rather than a single developer productivity tool. It says the main challenge shifts from model output security to system design, governance, cost control, and orchestration as AI moves into production. The piece also suggests smaller, specialized models may be better suited than frontier models like Claude, Gemini, and GPT-5 for many SDLC tasks.

• 4InfoQ·7/24/2026research
  Self-Building Agent Experiment with LangChain4j

  The article describes an experiment in which a code assistant used LangChain4j documentation to design an agentic system. The assistant built a framework that could write, test, and debug code autonomously, with supervisor and workflow architectures showing different trade-offs between flexibility and debugging speed.

• 6The New Stack·7/18/2026research
  AI Agents Need Better Context Layers, Not Just Smarter Models

  The article argues that many AI agent reliability problems come from weak infrastructure around the model, not from the model itself. It highlights Karpathy’s workflow as an example of compiling raw knowledge into structured, queryable context, then using retrieval and tools to help agents operate more reliably.

• 6InfoQ·7/18/2026product_launch
  Pinecone Launches Nexus Engine for Structured Context for AI Agents

  Pinecone has made Nexus generally available as a knowledge engine that turns enterprise data into a structured layer agents can query directly. The company says this lets teams curate business context once and reuse it across agents, improving accuracy while lowering token costs.

• 4InfoQ·7/17/2026research
  CNCF Says Cloud-Native Infrastructure Will Underpin Agentic AI

  A technical analysis from the Cloud Native Computing Foundation argues that agentic AI will likely run on the existing cloud-native stack rather than entirely new infrastructure. It frames cloud-native technologies as the foundation for building trustworthy, distributed AI systems.

• 6InfoQ·7/15/2026framework_update
  Stripe Benchmarks AI Agents on Real Integrations

  Stripe introduced a benchmark suite to test whether AI agents can build real-world Stripe integrations across backend, frontend, and browser-based checkout flows. The study highlights that these systems can generate integrations, but still struggle with execution, testing, and validation under production-like constraints.

• 7The New Stack·7/14/2026funding
  Thira raises $21M to build an AI execution layer for enterprise IT

  Thira, founded by Apptio co-founder Sunny Gupta and Kurt Shintaffer, is coming out of stealth with a $21 million seed round led by Madrona. The company is building an agentic system of execution for enterprise back-office work, starting with IT processes that span many internal systems and will launch with a semi-autonomous mode before offering full autonomy.

• 7InfoQ·7/13/2026product_launch
  DoorDash Details AI Shopping Assistant Built Beyond a Single LLM

  DoorDash described the architecture behind Ask DoorDash, a conversational shopping assistant that combines LLMs with specialized agents, MCP-based tooling, and an intelligence layer. The system uses persistent consumer memory and live backend data, with early results showing higher checkout conversion, larger baskets, and improved intent accuracy.

• 6The New Stack·7/11/2026research
  Why AI demos fail to reach production

  The article argues that many AI projects stall after impressive demos because production systems need real-time, trustworthy data from multiple sources, not curated sample inputs. A Confluent report cited in the piece says only 32% of organizations have agentic AI in production, while most IT leaders point to data infrastructure, data quality, and skills gaps as the main barriers.

• 7The New Stack·7/9/2026breakthrough
  Jensen Huang says AI agents are replacing traditional coding

  Nvidia CEO Jensen Huang said software development is shifting away from manual syntax writing and toward building agentic AI systems. He argued that engineers will spend more time creating agents, evaluation frameworks, and safety guardrails while AI handles repetitive coding tasks.

• 7Google DeepMind·4/21/2026framework_update
  Google DeepMind partners with consultants to scale enterprise AI

  Google DeepMind is partnering with Accenture, Bain, BCG, Deloitte, and McKinsey to help organizations adopt frontier AI at scale. The initiative focuses on industry-specific AI solutions, early access to Gemini models, and direct access to DeepMind leadership for enterprise customers.

• 8Anthropic News·1/27/2026product_launch
  Anthropic to pilot Claude-powered AI assistant for GOV.UK

  Anthropic has been selected by the UK Department for Science, Innovation and Technology to help build and pilot an AI assistant for GOV.UK services. The initial focus is employment support, with the system designed to guide users through government processes, provide tailored advice, and help citizens access training and job-search resources safely.

• 5OpenAI Blog·1/8/2026research
  Netomi’s enterprise agent scaling lessons

  OpenAI highlights how Netomi scales enterprise AI agents by combining concurrency, governance, and multi-step reasoning to support reliable production workflows. The example centers on using GPT-4.1 and GPT-5.2 to run agentic systems at enterprise scale.