Agentic apps in 2026: from chatbot to product workflow
AI agents are moving from chat windows into real workflows: planning, tool use, memory, approvals and product telemetry.
AI agentsproduct designautomation
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MCP servers: the new integration layer for AI tools
Model Context Protocol gives AI systems a standard way to work with tools, resources and prompts across products.
MCPintegrationtooling
Open articleMobile AI: when to run models on-device and when to use cloud
Modern mobile apps can mix on-device intelligence with cloud models for privacy, latency and richer reasoning.
mobile appson-device AIprivacy
Open articleDesigning AI copilots for web applications
A useful copilot is contextual, explainable and close to the user’s current task rather than a disconnected chat widget.
web appscopilotsUX
Open articleAI in educational games: personalisation without losing play
AI can adapt learning paths, hints and feedback while preserving the joy of discovery.
edtechgamespersonalisation
Open articleSecure AI tool use: permissions, audit logs and human approval
Agentic systems become safer when every tool call is permissioned, observable and reversible where possible.
AI securityagentsgovernance
Open articleAI-native backends: queues, traces and model-aware APIs
Backend systems for AI features need streaming, retries, observability and context management from day one.
backendobservabilityAI infrastructure
Open articleMultimodal interfaces: text, voice, images and app context
Users increasingly expect apps to understand screenshots, voice, documents and structured app state.
multimodal AIUXvoice
Open articleSEO for Angular SSR apps: what actually matters
SSR gives crawlers HTML, but rankings still depend on content quality, metadata, internal links and performance.
AngularSSRSEO
Open articleThe future of IT development: smaller teams, stronger tools
AI-assisted development is shifting teams toward faster prototypes, better tests and more product thinking.
software developmentAI codingproduct teams
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