#context engineering

Article: Agentic AI Architecture: Current Challenges and Future Opportunities

Agentic AI architecture extends microservices and cloud-native architectures with AI agents that work with context, memory, and protocols such as MCP and A2A. The article covers bounded-context integration, task-dependent coordination strategies, new security risks, and observability needs. It expects gradual adoption alongside existing architectures, with platforms absorbing common components. By Rafał Gancarz

InfoQ · EN

Глубоководье ИИ инженерии

Как устроена моя рабочая среда для ИИ-агентов: общая база правил и памяти, выбор контекста по задаче, навыки с ограниченными правами, проверки корректности и передача работы между агентами. Разбираю устройство Jarvis на своих проектах и показываю, что можно установить и попробовать из открытого репозитория. Как устроен Jarvis

Habr · RU

Article: Systemic Approach to Memory, Knowledge, and Context in Agentic AI Architectures

The article argues that reliable agentic AI architecture depends on a context engine that selects, injects, compresses, and expires information before it reaches the model. It separates context, memory, and knowledge, and recommends tiered memory, curated knowledge, and token budgeting. It also covers common anti-patterns, such as context stuffing and stale data loops, with mitigations. By Adi Polak

InfoQ · EN

Presentation: Beyond the Harness: Canvas for AI Agents

Nimisha Asthagiri shares how applying proven product engineering and architecture principles creates a bounded canvas for AI agents. She explains how this model provides agents with bounded autonomy, continuous drift detection, and decision records to scale software development resiliently. By Nimisha Asthagiri

InfoQ · EN