InfoQ Homepage AI Architecture Content on InfoQ
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Inkitt Builds a One-Shot PR Creator with Resumable Agent Orchestration
Inkitt built a one-shot pull request creator that automates small, low-risk code changes from task intake to draft PR creation. Its event-driven orchestrator persists workflow state, coordinates short-lived coding agent containers, bounds retries, and enforces permission restrictions while retaining human review and approval before changes can be merged.
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DoorDash Cuts Grocery Reorder Latency from 16 to 2 Seconds with AI Agents
DoorDash reduced grocery reorder latency from 16 to 2 seconds by precomputing recurring purchase needs using batch LLM inference and deterministic validation. Live catalog resolution keeps product selections current, while an editable shopping interface avoids unnecessary model calls. The company reports that shopping list views doubled and the flow’s order rate increased by approximately 34%
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Production AI and Engineering Team Design: InfoQ’s November Certification Cohorts
A preview of three November InfoQ online certification cohorts covering production AI systems, retrieval and agent design, engineering team flow, AI roles, and measurement, facilitated by Adi Polak, Lan Chu, and Olimpiu Pop.
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Production Patterns for Agents, Evals, and LLM Costs: QCon AI New York 2026
Once AI is in production, code arrives faster than it can be reviewed, token costs rise, evals have to be good enough to act on, and agents need permissions and guardrails. QCon AI New York 2026 (December 15-16) has confirmed sessions from engineers sharing how they handle these constraints.
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QCon London 2027 Announces 15 Tracks on Production AI, Architecture, and Engineering at Scale
A preview of the 15-track QCon London 2027 program, covering agent evaluation and guardrails, AI-era architecture, distributed-system debugging, modern data platforms, high-performance engineering, and Staff+ leadership.
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Akka Tests Spec-Driven AI Delivery across 65 Open Source Projects
Akka used 65 open-source projects to examine how specification structure, context, model selection, automated validation, and delivery guardrails affect AI assisted software porting. The experiment measured time, token use, code size, test parity, and performance, finding substantial variation across models, effort levels, and project types.
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Docker Sandbox Kit Spec: Packaging AI Agent Permissions as OCI Images
Docker has announced that it is bringing the Sandbox Kit Specification to the CNCF, aiming to make what an AI agent may access as portable as the agent itself.
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TypeSafe AI Releases Jev: a Decision-Only Model That Returns Typed Probabilities Instead of Text
TypeSafe AI, founded by former OpenAI researcher Diogo Almeida, has introduced Jev, a decision-making model that generates typed outputs rather than text. It evaluates inputs in parallel, providing results with probabilistic scores and confidence values. Jev's adoption has been swift, with integrations into platforms like Vercel and Netlify, highlighting its efficiency over traditional models.
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Cursor Uses S3 WAL to Scale Git Storage to More than 300 Pushes per Second
Cursor has introduced Continuity, a Git storage architecture that uses an S3 backed write ahead log as the source of truth. The design turns local NVMe repositories into warm caches and separates replica coordination from consistency. Cursor reports linear read scaling with up to 100 replicas and more than 300 pushes per second with S3 Express One Zone in synthetic tests.
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Stateless MCP Removes Session Affinity Requirements for AWS Server Deployments
AWS details how the latest Model Context Protocol specification removes protocol-level sessions, sticky-session requirements, and session storage for remote MCP servers. The change enables independent request routing and simpler horizontal scaling while shifting application state, retries, observability, and idempotency concerns to other layers.
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From Agent Authorization to AI Production Evaluation: QCon AI New York 2026
QCon AI New York has confirmed 23 sessions covering agent authorization, production guardrails, shared inference infrastructure, and the evaluation of AI systems after deployment.
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DoorDash Uses Multi Agent LLMs to Clean up 60,000 Feature Flags
DoorDash built a multi-agent LLM system to automate stale feature flag cleanup across more than 60,000 flags and 623 repositories. The workflow combines live experimentation data through MCP, engineer approval, isolated Git worktrees, parallel agents, and automated validation. In an evaluation of 50 flags, 45 produced usable pull requests at an average of 13.8 minutes and $4.79 per cleanup.
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Kubernetes Multi-Cluster Project Karmada Reaches CNCF Graduation
The Cloud Native Computing Foundation (CNCF) announced on September 2026 that Karmada, a multi-cluster and multi-cloud Kubernetes orchestration project, has graduated. This multi-cluster and multi-cloud Kubernetes orchestration project reached CNCF's highest maturity tier.
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Dropbox Outlines How Focusing on Existing Infrastructure Efficiency Can Create Headroom for AI
Dropbox has outlined how a decade of infrastructure optimization is helping it absorb growing demand from AI without treating new data-center capacity as the only answer. Its work spans forecasting, fleet utilization, storage density, hardware lifecycles, and rack-level power delivery, much of it predating the current AI boom.
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HCP Terraform Positions Itself as the Control Plane for AI-Driven Infrastructure
HashiCorp is positioning HCP Terraform as the governance and control plane for a new generation of AI-driven infrastructure, arguing that the rapid adoption of coding agents is shifting the biggest infrastructure challenge from writing configuration to verifying and safely executing it.