AGENTIC AI: ARCHITECTURE PATTERNS AND WHAT TO MEASURE BEFORE YOU SHIP
A new survey consolidates how LLM-based agents are built—policy/LLM core, memory, planners, tool routers, and critics—plus orchestration choices (single vs multi-agent) and deployment modes. It highlights practical trade-offs (latency vs accuracy, autonomy vs control) and evaluation pitfalls like hidden costs from retries and context growth, and the need for guardrails around tool actions. Benchmarks such as WebArena, ToolBench, SWE-bench, and GAIA illustrate task design and measurement under real constraints.
Articles
- https://arxiv.org/html/2601.01743v1
- https://petronellatech.com/blog/beyond-rpa-ai-agents-automate-the-back-office/
- https://quantumzeitgeist.com/ai-systems-framework-achieves-multimodal-prompt-injection/
- https://blog.dailydoseofds.com/p/layers-of-observability-in-ai-systems
- https://www.infoworld.com/article/4112542/how-to-make-ai-agents-reliable.html
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Agentic AI: architecture patterns and what to measure before you ship
A new survey consolidates how LLM-based agents are built—policy/LLM core, memory, planners, tool routers, and critics—plus orchestration choices (single vs multi-agent) and deployment modes. It highlights practical trade-offs (latency vs accuracy, autonomy vs control) and evaluation pitfalls like hidden costs from retries and context growth, and the need for guardrails around tool actions. Benchmarks such as WebArena, ToolBench, SWE-bench, and GAIA illustrate task design and measurement under real constraints.