InfoQ Homepage Agents Content on InfoQ
-
Multi-Agent Patterns from Spotify’s AI Powered Advertising Platform
Pratik Rasam explains how Spotify Ads Manager builds multi-agent AI systems in production using Google ADK Java, detailing guardrails, domain ownership, and key architectural patterns for scale.
/presentations/spotify-multi-agent-ai-architecture/en/mediumimage/pratik-rasam-medium-1790847111671.jpeg)
-
Building Reusable Evaluation Frameworks for Agentic AI Products
Susan Chang shares how Elastic built a production-grade AI agent evaluation framework, detailing lessons on tracing, LLM-as-a-judge, programmatic evals to prevent regression across engineering teams.
/presentations/elastic-ai-agent-evaluations/en/mediumimage/susan-chang-medium-1790846586191.jpeg)
-
Context is the New Code
Patrick Debois explains how treating context like code transforms the Software Development Life Cycle into a Context Development Life Cycle to scale AI agent workflows reliably across teams.
/presentations/context-as-code-devops-agents/en/mediumimage/patrick-medium-1789632704652.jpeg)
-
From Consumers to Builders: Turning 200 of our Team into Agent Creators in Two Weeks
Ben Maraney explains how Forter enabled R&D teams to build internal AI agents fast by simplifying tools, using custom MCP servers, and removing organizational roadblocks to maximize engineering ROI.
/presentations/building-internal-ai-agents/en/mediumimage/medium-1790246283432.jpeg)
-
APIs for Agents: Rethinking API Programs in the MCP Era
Jim Gough and Andreea Niculcea explain how Morgan Stanley leverages Architecture as Code with CALM and MCP to scale secure, compliant API platforms for AI agents and future protocols like A2A.
/presentations/mcp-calm-api-architecture/en/mediumimage/jim-gough-andreea-niculcea-medium-1789021410351.jpg)
-
The Agent Harness: Control Planes, Invariants, and Approval Boundaries for Production AI Agents
OpenAI’s Vinoth Govindarajan explains how to build reliable AI agent harnesses in production, focusing on state ownership, mutation ordering, scoped authority, and user-visible proof of action.
/presentations/ai-agent-harness/en/mediumimage/vino-medium-1789631870270.jpeg)
-
Context Engineering at LinkedIn: How We Built an Organizational Context Layer for AI Agents with MCP
Ajay Prakash shares how LinkedIn built Contextual Agent Playbooks and Tools to give AI coding agents internal stack context, automating 600+ workflows and raising productivity 20%.
/presentations/linkedin-context-engineering/en/mediumimage/ajay-prakash-medium-1789021803159.jpg)
-
Decision Models in Agentic Architectures: from Production to Agent Skills
Alex Porcelli explains how combining deterministic DMN decision models with agentic AI resolves enterprise AI challenges around accountability, rule governance, and non-deterministic logic.
/presentations/decision-models-agentic-ai/en/mediumimage/alex-porcelli-medium-1789021621084.jpeg)
-
From Retrieval to Reasoning: Building Production-Ready Agentic AI Systems with Knowledge Graphs
Cassie Shum shares lessons learned on using knowledge graphs as the foundation for agentic software systems, covering 4 key architectural patterns to optimize context, provenance, and team efficiency.
/presentations/knowledge-graphs-agentic-systems-patterns/en/mediumimage/cassie-shum-medium-1788338916792.jpeg)
-
Fixing the AI Infra Scale Problem by Stuffing 1M Sandboxes in a Single Server
Felipe Huici discusses scaling microVM sandboxes for AI workloads, explaining how millisecond cold boots, high density, and stateful scale-to-zero achieve efficient, secure cloud isolation.
/presentations/unikraft-microvm-sandboxes-cloud-scaling/en/mediumimage/felipe-huici-medium-1787813506607.jpg)
-
From AI Agent Demo to Production: Automated Testing and Evaluation
Zhou Yu explains why 95% of AI agents fail to reach production and shares how simulation-driven evaluation, synthetic users, and automated CI/CD testing unlock enterprise deployment.
/presentations/ai-agent-testing-evaluation/en/mediumimage/zhou-yu-medium-1787813732120.jpeg)
-
A Few Predicted Talks From QConAI 2030
Meryem Arik shares 2030 predictions on rising token costs, parallel agent architectures, non-developer app explosion, vendor lock-in, incoming AI regulations, and shifting software engineering roles.
/presentations/ai-predictions-2030/en/mediumimage/meryem-arik-medium-1785845650090.jpg)