App Development Studio for AI Agents, Mobile & Web Apps

Building practical AI Agents: Architectural Scope and Evaluation Trade-Offs

How to build production-ready AI agents by scoping narrow use cases, building rigorous evaluation sets, and implementing minimal loops before adding human review.

Adapting Decoder-Only Models to Encoder-Decoder Architectures for Inference Efficiency

T5Gemma adapts pretrained decoder-only Gemma 2 models into encoder-decoder topologies. This approach improves inference efficiency and input representation for text tasks.

MongoDB Atlas Agent Engine Solves State and Governance Bottlenecks for practical AI Agents

MongoDB Atlas Agent Engine provides a unified execution, memory, and governance layer. It helps engineering teams deploy production-ready AI agents without stitching together disparate infrastructure.

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.

Agent Development Kit (ADK) for Kotlin 1.0: practical Multi-Agent Architecture and Android Integration

Google releases ADK for Kotlin 1.0, delivering a model-agnostic multi-agent orchestration toolkit with zero-reflection KSP tooling and native Android persistence.

Scaling AI Agent Workflows: Overcoming the Production Pilot Gap for practical AI Agents

Enterprise technology surveys reveal that while 78 percent of organizations run AI agent pilots, only 14 percent reach production. Bridging this gap requires continuous evaluation infrastructure rather than prompt adjustments.

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.

Moving Beyond Prototypes: Engineering practical AI Agents

Transitioning AI agents from simple prompt-response loops to enterprise-grade systems requires addressing latency, context management, and infrastructure scalability.

Implementing Security Guardrails in Agent Development Kit (ADK) Architectures

Secure your AI agents by implementing granular identity management and tool-level access controls within the Agent Development Kit framework.

Treating AI Agents as Production Workloads: The Governance Gap

Most enterprises run AI agents on infrastructure never built for them. Platform teams must bridge the governance gap to move from experimental pilots to production-ready systems.

practical AI Agents: Integrating OpenTelemetry Traces into Unity Catalog

Move beyond siloed observability by streaming OpenTelemetry traces directly into Unity Catalog. This architecture enables governed, analytics-ready AI agent monitoring.

Why AI Agents Fail in Production: 6 Failure Modes & Fixes

Production-ready AI agents often fail due to engineering design flaws rather than model limitations. Learn to mitigate context drift and memory issues.

AI Agent Orchestration: Solving the Engineering Workflow Gap

Engineering AI projects often stall because they target isolated tools. Orchestration succeeds by automating the manual handoffs between systems.

Architecting practical AI Agents: Beyond Performance Metrics

Moving AI agents to production requires shifting from simple performance monitoring to deep observability of reasoning chains and decision-making reliability.

Architecting practical AI Agents: Beyond One-Off Scripts

Move beyond experimental scripts by implementing event-driven architectures and session-aware observability. Learn how to handle state, interrupts, and quality metrics in production.

Deploying Agent Development Kit Agents to Google Cloud

Transitioning ADK agents from local development to production requires choosing between serverless Cloud Run or orchestrated GKE. This guide outlines the specific deployment paths for each.

Agent Versioning for AI Agents: How to Safely Release Prompts, Tools, and Policy

How to implement versioned manifests for AI agents to ensure reproducibility and safe production releases. Avoid the pitfalls of mixing prompt, tool, and policy changes in a single state.

Observability Frameworks for practical AI Agents

Moving beyond standard model monitoring requires tracking multi-step reasoning and tool usage. Learn how to distinguish between performance and quality metrics in agentic systems.

Moving Beyond Static Benchmarks for practical AI Agents

Static benchmarks fail to capture the complexity of agentic workflows. Learn why environment-driven evaluation is the new standard for production reliability.

Why AI Agents Need Their Own Permission Model

Traditional identity models fail to secure autonomous agents. Learn how to implement task-scoped, least-privilege architectures for non-deterministic AI workflows.

Architecting Stateful Services for practical AI Agents

Move beyond proof-of-concepts by treating AI agents as stateful, modular services. Learn how to implement session routing and task deduplication for reliable production deployments.