AI Agents

The search engine is the heart of every e-commerce platform. If customers cannot find a product, they will not buy it. Over the past decade, when referring to Search, we defaulted to Elasticsearch (with the BM25 algorithm). However, as user search behavior evolves—from typing abrupt keywords (“men’s running shoes”) to long queries full of complex intent (“find me waterproof trail running shoes, size 42, under $100, that can be delivered today”), traditional search engines begin to reveal their fatal flaws. ...

If you have ever tried to push a RAG or Multi-Agent system written in Python (using LangChain or AutoGen) into a Production environment with thousands of concurrent requests, you have likely tasted the pain. Servers run out of RAM, CPUs become bottlenecked, and latency skyrockets uncontrollably. The root cause does not lie in the LLMs. The root cause lies in the Orchestration Architecture you are using. In Part 1 of this series, we will dissect why Python falls short in the Agentic era, and why Golang, combined with the Eino (CloudWeGo) framework, is the “ultimate weapon” for building the brain of next-generation e-commerce search systems. ...

1. The Decline of Static RAG In the previous 5 parts, we built a perfect RAG machine: real-time data (CDC), absolute security, and strict authorization. But no matter how perfect, traditional RAG suffers from a fatal flaw: It only knows how to “Read” and “Speak”, not how to “Do”. If you ask a RAG system: “Check if the server is overloaded, and if so, automatically boot up 2 more servers”, it will be completely powerless. RAG is a Static Pipeline running on a one-way street. ...

Welcome to this week’s Tech Radar. In our previous issue, we discussed Cloud-Native AI Architecture. Khi chúng ta đã có hạ tầng mạnh mẽ (Envoy, K8s Inference), vấn đề tiếp theo lập tức xuất hiện: Làm sao để kiểm soát bầy AI (AI Swarm) này? Đừng “thả rông” AI Agents trong production. Hôm nay, chúng ta đào sâu vào Zero-Trust Security cho Multi-Agent Swarms. 1. Tech News Radar: Lỗ hổng Agentic và sự trỗi dậy của Non-Human Identity Answer-first: Sự bùng nổ của AI Agents kéo theo rủi ro bảo mật nghiêm trọng (OWASP ASI02). Các API keys tĩnh không còn phù hợp; hệ thống đòi hỏi “Định danh cho Máy” (Non-Human Identity - NHI) qua công nghệ như SPIFFE để kiểm soát từng Agent độc lập. ...

Answer-first: Agentic E-commerce Search transforms traditional search from passive keyword matching to active shopping assistance using AI agents that understand complex queries, apply business logic filters, and provide personalized results in real-time. What You’ll Learn That AI Won’t Tell You Practical strategies for tuning vector search precision without bloating RAM. How to coordinate multiple AI search agents to prevent search query latency spikes. The search system is the beating heart of every e-commerce platform. If customers cannot find a product, they cannot buy it. However, as we move through 2026, user search behavior has evolved drastically from typing short, abrupt keywords (e.g., “men’s running shoes”) to submitting complex, goal-oriented queries (e.g., “find me a pair of men’s waterproof trail running shoes, size 42, under $100, that can be delivered by tomorrow”). Against these multifaceted intents, traditional search engines begin to show their limitations. ...

Today’s Tech Radar tracks three massive architectural shifts occurring simultaneously across the backend, frontend, and infrastructure ecosystems in 2026. On the backend, the Dapr project has stabilized its Agents v1.0 framework for Agentic AI. On the frontend, React Three Fiber (R3F) has successfully bridged the gap to WebGPU via the Three Shading Language (TSL). At the infrastructure layer, the upcoming Argo CD 3.4 release introduces critical “Day 2” operational safety mechanisms for Kubernetes GitOps. ...

DigitalOcean’s April 28, 2026 launch of its AI-Native Cloud is not the largest AI infrastructure announcement of the week, but it may be one of the clearest. Instead of treating AI as a feature added onto a legacy cloud, DigitalOcean is explicitly reorganizing its platform around what production AI systems now look like: multi-model inference, retrieval, routing, state, and long-running agent workflows. That framing matters because it captures a broader industry shift. Teams are moving away from the old pattern of “call one model and return one answer” toward systems that route prompts, retrieve private context, execute tools, and optimize cost across repeated loops. In that world, the hard problem is no longer just model access. It is operating the surrounding system cleanly. ...

The most important AI market signal of the last 24 hours is not a single model launch. It is the speed at which the ecosystem reacted once OpenAI’s Microsoft exclusivity ended. In one day, AWS converted OpenAI’s new multi-cloud freedom into a Bedrock distribution product, while Anthropic pushed Model Context Protocol further into the creative software stack. Taken together, these developments show that the market has already moved beyond the old question of who has access to the frontier model. The new competition is about who controls the runtime, who owns the connector layer, and who turns model capability into governable enterprise workflows. ...

One day after OpenAI rewrote its partnership with Microsoft, Amazon moved immediately to capitalize on the opening. On April 28, 2026, AWS announced a major expansion of its OpenAI partnership: the latest OpenAI models are now coming to Amazon Bedrock in limited preview, Codex is coming to Bedrock, and Amazon Bedrock Managed Agents powered by OpenAI are launching as well. This is not just another model-availability announcement. It is the first serious proof that OpenAI’s new multi-cloud posture is becoming a real distribution strategy rather than a contractual option. The timing matters. On April 27, 2026, OpenAI formally ended Microsoft’s exclusivity while keeping Azure as its primary cloud. On April 28, AWS turned that policy shift into a product. ...

Mistral released Small 4 this week — a 119B parameter model that consolidates what previously required three separate models. Under the Apache 2.0 license and optimized for both latency and throughput, Small 4 represents a strategic inflection point in the open-source model ecosystem. The key innovation is not just technical performance. It is the unified architecture: Mistral has merged the capabilities of Magistral (reasoning), Pixtral (multimodal), and Devstral (agentic coding) into a single model with configurable behavior. Users no longer switch between specialized models — they configure one model to deliver fast responses, deep reasoning, or visual analysis as the task demands. ...

Anthropic shipped two things this week that reframe how engineering teams will build AI agents. First, Claude Sonnet 4.5 — explicitly labeled “the best coding model in the world” — with substantial gains in reasoning, math, and computer use. Second, and more consequentially for platform teams, they open-sourced the Claude Agent SDK: the actual infrastructure that powers their frontier products. This is not an incremental model update. It is a strategic move to own the infrastructure layer of the emerging agent ecosystem, positioning Anthropic as both the model provider and the toolchain standard for complex agentic systems. ...

DeepSeek officially released the DeepSeek-V4 model series this week, continuing its trend of delivering frontier-level capabilities at a fraction of the computing cost. Released under the open-source MIT License, this update introduces two main model variants designed for high efficiency, long context, and agentic workflows. After reviewing the release announcement and technical details, it is clear that DeepSeek is no longer just competing on price — they are actively shaping how open-source models integrate into complex, multi-agent command centers and enterprise environments. ...

OpenAI shipped two things this week that belong together: the Codex desktop app for macOS (with Windows following in March) and GPT-5.2-Codex, a version of GPT-5.2 further optimized for agentic coding. After reading the full source material from both announcements, the picture that emerges is not an incremental model update. It is a deliberate architectural shift in how OpenAI thinks about the relationship between developers and AI agents. The framing in the Codex app announcement is precise: “The core challenge has shifted from what agents can do to how people can direct, supervise, and collaborate with them at scale.” That is a meaningful statement. It acknowledges that the bottleneck is no longer model capability — it is the tooling for managing agents at the scale that frontier models now make possible. ...

Google Cloud Next ‘26 ran in Las Vegas on April 22-23, 2026. After reading the full source material from the conference announcements, the picture that emerges is not a product update cycle. It is a strategic repositioning. Google Cloud CEO Thomas Kurian’s framing was explicit: “The experimental phase is behind us. How do you move AI into your entire enterprise? The answer is a unified stack.” Three interlocking bets define the announcement set. First, the Gemini Enterprise Agent Platform consolidates Google’s fragmented AI tooling into a single surface for building, running, and governing autonomous agents. Second, the eighth-generation TPUs split into two purpose-built variants — one for training, one for inference — reflecting a fundamental shift in how Google thinks about AI infrastructure economics. Third, Workspace Intelligence attempts to turn Google’s productivity suite into a shared knowledge layer that agents can reason across, not just a collection of isolated apps. ...