App Development Studio for AI Agents, Mobile & Web Apps

AI Coding Agents in Production: Scoped Delegation and Executable Test Verification

Modern AI coding agents require precise repository boundaries and strict test verification instead of sprawling prompts. Tech leads can maintain engineering control through scoped task delegation.

HumanLayer CRISPY Framework for AI Coding Agents in Production

HumanLayer replaces monolithic RPI prompts with the seven-stage CRISPY framework to improve reliability and code ownership when running AI coding agents in production.

AG-UI Protocol: Standardizing Agent-to-User Interaction and Interrupt Models

AG-UI standardizes agent-to-user interaction by replacing custom WebSockets with native event streaming, declarative UI generation, and shared state synchronization.

Configuring Agent Permissions: Designing Interruptible Workflows and Approval Gates for AI Side Effects

How to structure agent permissions and interruptible execution states to secure AI workflows against unintended side effects.

Building practical AI Agents with Decision Models and LangGraph Orchestration

How combining specialized decision models with deterministic code orchestration in LangGraph helps engineering teams build production-ready AI agents.

Google Agent Development Kit (ADK) Multi-Agent Orchestration: Solving Shared State Consensus and Fan-Out Bottlenecks

How to manage multi-agent consensus and prevent race conditions when scaling Google Agent Development Kit (ADK) parallel workflows.

Scaling Legacy Refactoring with AI Agents and Code Health Quality Gates

Scaling legacy refactoring requires objective code health gates rather than raw code generation. Architectural validation prevents AI agents from compounding technical debt.

Scaling AI Code Review Architecture with LangGraph and Semantic Kernel

Engineering teams face severe pull request bottlenecks and inconsistent review quality. Implementing a multi-agent AI code review architecture using Python LangGraph and C# Semantic Kernel automates security and performance checks.

How to Deploy AI Agents: Enterprise Adoption Realities and Architectural Trade-Offs

Recent survey data shows over half of organizations now run AI agents in production. Engineering teams face critical non-deterministic system challenges when moving from pilot to durable architecture.

Evaluating Tools for AI Agents: Execution Loops, Tool Calling, and Architectural Trade-Offs

An analysis of tools for AI agents, focusing on execution loops, tool calling mechanisms, and architectural trade-offs for production systems.

Applying the AI Gateway Architecture Pattern to Isolate Rapid Model and Agent Churn

How the AI gateway architecture pattern isolates rapid changes in models and agents. This protects core enterprise systems from integration churn.

Scale AI Workloads: Categorizing LLM Inference Patterns to Avoid Production Bottlenecks

How to categorize distinct LLM inference workloads to scale AI architectures efficiently in production. Avoid common pitfalls tied to model API dependencies and hidden infrastructure costs.

Expo App Development: Managing EAS Observe GA Pricing and CLI Migration Paths

Expo moves EAS Observe to general availability with updated tiered pricing and data retention rules. Meanwhile, teams must migrate away from the retiring Expo Agent by July 2026.

Evaluating AI Agents: A Production Blueprint with Strands and AgentCore

How Motorway and AWS built an end-to-end evaluation pipeline for production-ready AI agents, reducing incorrect search results from 1 in 8 to 1 in 50.

Deterministic Tool Calling: Guardrails for Autonomous AI Agents in Production

Secure your agentic workflows by implementing runtime schema validation, execution sandboxing, and strict cost controls. Prevent runaway loops and unauthorized system access.

Moving Beyond Frameworks: The Case for an Agent Operations Fabric

Production-grade AI agent orchestration requires more than just a framework. An agent operations fabric provides the governance, auditability, and multi-vendor control necessary for enterprise reliability.

Loop Engineering: Structuring AI Coding Agents for Reliable Software Delivery

Move beyond single-shot prompting by implementing feedback loops that allow AI coding agents to observe, validate, and refine code iteratively.

Moving Beyond HTTP Logs: A Three-Pillar Architecture for Agent Observability

Traditional monitoring fails for non-deterministic AI agents. Implement a three-pillar architecture using traces, evals, and debug loops to gain visibility into agent decision paths.

Architecting AI Agent Orchestration: Beyond Simple Pipelines

Orchestration design is the primary failure point in enterprise agent systems. Learn to select the right pattern to manage complexity and system reliability.

Production AI Agent Observability: Monitoring, Debugging, and Cost Control at Scale

Moving AI agents to production requires more than standard logs. Effective observability must integrate cost telemetry and evaluation feedback loops to maintain system reliability.

Moving Beyond Deterministic Testing: A Framework for Agentic AI Evaluation

Agentic systems require evaluation strategies that account for non-deterministic reasoning and multi-step tool use. Learn how to shift from static assertions to workflow-aware metrics.