#Antonio Gulli
14 articles tagged #Antonio Gulli.
Prompt Chaining: Building Reliable AI Agent Workflows
Prompt chaining breaks a complex task into a sequence of LLM calls, where each step's output feeds the next. It is the foundational pattern for reliable AI agents.
Routing and Parallelization: Scaling AI Agent Orchestration
Routing dispatches each input to the right specialized path; parallelization runs independent sub-tasks at once. Together they scale agent accuracy and latency.
Reflection and Adaptation: How AI Agents Learn From Their Own Output
Reflection is the pattern where an AI agent critiques its own output and revises it, looping until the work clears a quality bar. It is the self-correction loop behind reliable agents.
AI Agent Planning: How Intelligent Systems Decide What to Do Next
AI agent planning turns a high-level goal into an ordered sequence of executable steps. Learn task decomposition, plan-and-execute vs ReAct, and when to replan.
Reasoning Techniques for AI Agents: Chain-of-Thought to Tree-of-Thought
Chain-of-Thought, self-consistency, ReAct, and Tree-of-Thought are the reasoning architectures that separate toy AI demos from production agents.
Agent-to-Agent Communication: How A2A Enables Agent Interoperability
A2A (Agent2Agent) is an open protocol that lets agents from different vendors discover each other, advertise capabilities, and delegate tasks across systems.
Memory Management for AI Agents: Short-Term, Long-Term, and Beyond
How AI agents remember across a turn and across sessions: short-term context, long-term episodic, semantic, and procedural memory, and how it differs from RAG.
AI Guardrails and Safety: Building Trustworthy Agentic Systems
AI guardrails are the input, output, and permission controls that keep an agent safe in production: what separates a demo from an enterprise deployment.
Exception Handling and Human-in-the-Loop: Making AI Agents Resilient
How AI agents fail gracefully: retries, fallbacks, and circuit breakers for tool failures, plus human-in-the-loop approval gates for high-risk actions.
Monitoring AI Agents: Goal Setting, Evaluation, and Prioritization
You can't improve an AI agent you can't measure. A practical guide to observability, offline and online evals, the metrics that matter, and what to fix first.
Resource-Aware AI Agents: Optimization and Exploration Strategies
Resource-aware AI agents budget tokens, route tasks by difficulty, cache results, and stop early. The patterns that separate a viable product from a money pit.
Industry Leaders on Agentic AI: Perspectives from Google and Goldman Sachs
What Google's Saurabh Tiwary and Goldman Sachs' Marco Argenti reveal about how enterprises build, govern, and get ROI from agentic AI.
The Definitive Glossary of Agentic AI: 100+ Terms Explained
A 100+ term glossary of agentic AI, from agents and the agentic loop to MCP, RAG, reflection, guardrails, and evals. Clear definitions for teams building agents.
Agentic Design Patterns: The Complete Guide to Building Intelligent AI Systems
A 19-part series on the design patterns behind production AI agents, based on Antonio Gulli's Agentic Design Patterns. From prompt chaining to multi-agent orchestration.