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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.

AI Agent Security Starts With Permissions, Not Prompts

Secure AI agents by decoupling tool access from model prompts. Implement granular permission scopes and risk-tiered tool architectures to prevent unauthorized data exposure.

Moving Beyond Chat: Architecting Task-Oriented AI Agent Systems

Shift from conversational chatbots to task-oriented agent systems by prioritizing tool calling and multi-agent orchestration. Learn how to implement task closure in your production architecture.

Moving Beyond Model Benchmarks: Engineering Agent Evaluation Workflows

Shift from static model benchmarks to dynamic agent evaluation to ensure reliability in production. Learn how to design multi-turn tests that account for tool usage and state changes.

Reducing Agentic Latency with Programmatic Tool Calling

Programmatic tool calling replaces sequential LLM round-trips with sandboxed code execution. This pattern reduces latency and token costs for complex agentic workflows.

Multi-agent orchestration patterns: a 2026 production guide

Moving beyond simple agent demos requires choosing the right orchestration pattern. Understand the production trade-offs of supervisor, swarm, and hierarchical architectures.

AI Agents vs. Agentic AI: Distinguishing Automation from Reasoning

Distinguishing between simple task automation and true agentic reasoning is essential for avoiding technical debt in AI infrastructure. Learn how to evaluate these systems based on their ability to resolve complex, multi-step problems.

Building an Evaluation Harness for Production AI Agents: A 12-Metric Framework From 100+ Deployments

Move beyond simple unit tests for AI agents. Implement a 12-metric evaluation framework to measure retrieval, generation, and agent behavior in production.

Architecting practical AI Agent Guardrails for Operational Resilience

Move beyond simple prompt filtering to build resilient agentic systems. Runtime orchestration, access control, and automated recovery paths.

Automated Quality Gates for LLM-Integrated Product Engineering

Traditional testing fails to capture non-deterministic LLM behavior. Implementing evidence-based quality gates allows for automated release governance in agentic systems.

Multi-Agent Orchestration: Architectural Patterns for Complex Workflows

Move beyond monolithic AI prompts by implementing multi-agent orchestration. Learn how to choose between supervisor, pipeline, swarm, and hierarchical patterns.

Building Durable AI Agents with Google ADK: Moving Beyond Stateless Chatbots

How to implement persistent state management in AI agents using Google ADK. This approach enables long-running workflows that survive idle time and context loss.

Testing Agentic Systems: Managing Non-Determinism in Tool-Calling Workflows

Moving from static LLM prompts to stateful agentic loops introduces non-determinism. Learn how to build reliable testing frameworks for tool-calling architectures.