InfoQ Homepage AI, ML & Data Engineering Content on InfoQ
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/articles/building-hierarchical-agentic-rag-systems/en/smallimage/building-hierarchical-agentic-rag-systems-thumbnail-1775040657142.jpg)
Building Hierarchical Agentic RAG Systems: Multi-Modal Reasoning with Autonomous Error Recovery
In this article, the author explores how hierarchical agentic RAG systems coordinate specialized workers through structured orchestration to improve accuracy, reliability, and explainability in complex enterprise analytics workflows. The article uses Protocol-H as a to show how deterministic routing, reflective retry, and modality-aware reasoning support safer multi-source query execution.
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Stateful Continuation for AI Agents: Why Transport Layers Now Matter
Agent workflows make transport a first-order concern. Multi-turn, tool-heavy loops amplify overhead that is negligible in single-turn LLM use. Stateful continuation cuts overhead dramatically. Caching context server-side can reduce client-sent data by 80%+ and improve execution time by 15–29% .
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/articles/optimization-in-automated-driving/en/smallimage/optimization-in-automated-driving-thumbnail-1774348229685.jpg)
Optimization in Automated Driving: from Complexity to Real-Time Engineering
In this article, author Avraam Tolmidis discusses technical architecture of autonomous vehicles, with focus on optimization techniques like context-aware sensor fusion and Model Predictive Control (MPC) solvers to help with processing raw sensor data into safe control commands.
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/articles/architecting-autonomy-scale/en/smallimage/architecting-autonomy-scale-thumbnail-1774360140662.jpg)
Architecting Autonomy at Scale: Raising Teams without Creating Dependencies
Modern engineering needs a shift from "gates" to "guardrails." Scale via decentralized architecture that treats teams like adults—building judgment through Socratic coaching, shared platforms, and automated drift detection. Move beyond bottlenecks to an interdependent model where AI governance and ADRs preserve context without killing velocity. Empower autonomy while maintaining alignment.
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/articles/architectural-governance-ai-speed/en/smallimage/architectural-governance-ai-speed-thumbnail-1773997111820.jpg)
Architectural Governance at AI Speed
In the GenAI era, code is a commodity, but alignment is not. Traditional review boards can't scale with AI-generated output. This article explores "Declarative Architecture" - transforming ADRs and Event Models into automated guardrails. Move beyond "dumping left" to a model where the conformant path is the path of least resistance, enabling decentralized governance without losing cohesion.
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/articles/evaluating-ai-agents-lessons-learned/en/smallimage/evaluating-ai-agents-lessons-learned-thumbnail-1773307288872.jpg)
Evaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned
This article introduces practical methods for evaluating AI agents operating in real-world environments. It explains how to combine benchmarks, automated evaluation pipelines, and human review to measure reliability, task success, and multi-step agent behavior. The article also discusses the challenges of evaluating systems that plan, use tools, and operate across multiple interaction turns.
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/articles/oil-water-moment-ai-architecture/en/smallimage/the-oil-and-water-moment-in-ai-architecture-thumbnail-1773149586690.jpg)
The Oil and Water Moment in AI Architecture
Have you ever tried mixing oil and water? That is the moment software architecture is entering as deterministic systems meet non deterministic AI behaviour. Architects must anchor intelligent systems in intent, governance and systems thinking. This article introduces the Architect’s V Impact Canvas, a framework for navigating this shift while keeping human trust at the centre.
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Building a Least-Privilege AI Agent Gateway for Infrastructure Automation with MCP, OPA, and Ephemeral Runners
This article presents a least-privilege AI Agent Gateway that places clear controls between AI agents and infrastructure. Agents do not access infrastructure APIs directly. Instead, every request is validated, authorized using policy as code with Open Policy Agent (OPA), and executed in short-lived, isolated environments, with built-in observability using OpenTelemetry.
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/articles/architecting-agentic-mlops-a2a-mcp/en/smallimage/architecting-agentic-mlops-a2a-mcp-1770303550343.jpg)
Architecting Agentic MLOps: a Layered Protocol Strategy with A2A and MCP
In this article, the authors outline protocols for building extensible multi-agent MLOps systems. The core architecture deliberately decouples orchestration from execution, allowing teams to incrementally add capabilities via discovery and evolve operations from static pipelines toward intelligent, adaptive coordination.
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/articles/prompts-to-production-playbook-for-agentic-development/en/smallimage/prompts-to-production-playbook-thumbnail-1770374539263.jpg)
From Prompts to Production: a Playbook for Agentic Development
In this article, author Abhishek Goswami shares a practitioner's playbook with development practices, that describes building agentic AI applications and scaling them in production. He also presents core architecture patterns for agentic application development.
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/articles/building-llms-resource-constrained-environments/en/smallimage/building-llms-resource-constrained-environments-thumbnail-1770217548603.jpg)
Building LLMs in Resource-Constrained Environments: a Hands-On Perspective
In this article, the author argues that infrastructure and compute limitations can drive innovation. It demonstrates how smaller, efficient models, synthetic data generation, and disciplined engineering enable the creation of impactful LLM-based AI systems despite severe resource constraints.
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/articles/why-ml-projects-fail-production/en/smallimage/why-ml-projects-fail-production-thumbnail-1769072877092.jpg)
Why Most Machine Learning Projects Fail to Reach Production
In this article, the author diagnoses common failures in ML initiatives, including weak problem framing and the persistent prototype-to-production gap. The piece provides practical, experience-based guidance on setting clear business goals, treating data as a product, and aligning cross-functional teams for reliable, production-ready ML delivery.