App Development Studio for AI Agents, Mobile & Web Apps

Architecting Tools for AI Agents: Managing Multi-Agent Tool Orchestration and Authority Boundaries

Multi-agent systems require explicit control flow and typed tool contracts rather than conversational personas. Learn how to design tool boundaries for production AI workloads.

Building Generative UI with Blazor AI Components and Model Context Protocols

How new Blazor AI components stream agent interactions into UI content blocks. See how architectures handle state boundaries and tool integration safely.

Architecting Tools for AI Agents: Deterministic Pipelines Versus Dynamic Loops

Evaluate the architectural trade-offs between deterministic linear pipelines and dynamic tool-use loops when deploying production-ready AI agents.

Scaling 1000 AI Agents on Oracle Kubernetes Engine with Shared File Storage

Managing a fleet of 1000 autonomous AI agents requires infrastructure. Learn how OKE and OCI File Storage provide scalable shared POSIX workspaces for agent swarms.

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.

Generative UI Architecture Patterns for AI Agents in Production

How agent-rendered generative UI patterns replace static text with runtime components. Learn the trade-offs of sandboxed frames and client rendering.

Securing AI Agent Tool Access with MCP Gateways

As AI agents gain autonomous access to enterprise systems, traditional API security models fail. Implementing MCP gateways provides the necessary governance and audit trails.

Architectural Distinctions in Agentic Systems: Workflows vs. Autonomous Agents

Building effective AI agents requires a disciplined approach to architectural complexity. Many teams default to autonomous systems when simpler, more predictable patterns would suffice.

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.

Architecting Production-Grade Agentic AI Systems

Moving AI agents from demo to production requires a structured 7-layer architecture. Orchestration, tool exposure, and observability to manage scale.

Designing Zero-Trust Security for Autonomous AI Agents

Autonomous agents require granular permission models to mitigate security risks. Adopting zero-trust principles ensures that agent actions remain auditable and constrained.

Building AI Agents: Why State Management is the Step Most Tutorials Skip

Most AI agent tutorials stop at single-turn tool calls. Real-world reliability requires explicit state management to track task progress and prevent memory loss.

AI Agent Development: Architecture Patterns from 12 Deployments

Moving AI agents to production requires moving beyond simple prompting. Learn how to design tool schemas and state management to prevent compounding failure modes.

Beyond APM: Instrumenting the Decision-Making Layer for AI Agents

Standard APM tools fail to capture the non-deterministic nature of AI agents. Learn why instrumenting the decision-making layer is essential for production reliability.

Controlling AI Agents: Approval Workflows for Small Teams

Autonomous agents require explicit approval gates to operate safely in production. Learn how to implement tool-level human-in-the-loop controls to manage risk.