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Daniel Reyes
AUTHOR · AT SHIELDTHEMES SINCE 2021

Daniel Reyes

Lead AI Engineer

Daniel builds production AI agents for support, sales and operations teams. Before ShieldThemes he worked on search ranking systems.

AI agentsAutomationRAG

Articles by Daniel

14 published
AI Support Agent vs Chatbot: Which Fits Your Business?
AI · Oct 14, 2026 · 11 min
AI Support Agent vs Chatbot: Which Fits Your Business?
Explore how AI support agents and chatbots differ in handling customer support tasks, from scripted responses to connected actions that resolve issues effectively.
How to Train an AI Support Agent
AI · Oct 12, 2026 · 13 min
How to Train an AI Support Agent
This guide explains the step-by-step process to train an AI support agent, from defining its scope to testing and launching it safely in your support system.
How to Add an AI Chatbot to WordPress
AI · Oct 10, 2026 · 12 min
How to Add an AI Chatbot to WordPress
Explore the essential steps to integrate an AI chatbot into your WordPress site, focusing on approved content, human escalation, and secure installation for reliable customer interaction.
AI Agent Support Workflow Examples That Work
AI · Oct 06, 2026 · 12 min
AI Agent Support Workflow Examples That Work
Explore practical AI agent workflows that balance automation and human oversight to improve support quality across WordPress, Shopify, and CRM systems.
How we shipped a support agent that resolves 62% of tickets
AI · Sep 18, 2026 · 5 min
How we shipped a support agent that resolves 62% of tickets
The architecture, evals and guardrails behind a production AI agent — and the three mistakes we made first.
RAG for small teams: a knowledge base in two weeks
AI · Jul 28, 2026 · 5 min
RAG for small teams: a knowledge base in two weeks
A pragmatic stack for turning your docs into an assistant your team actually trusts.
Idempotent webhooks: building integrations that tolerate retries
Engineering · Jun 12, 2026 · 5 min
Idempotent webhooks: building integrations that tolerate retries
Webhooks arrive late, out of order and more than once. How we design handlers and integrations that stay correct whatever the sender does.
Fine-tuning versus better prompting: a practical decision guide
AI · May 12, 2026 · 5 min
Fine-tuning versus better prompting: a practical decision guide
When a fine-tuned model is worth the data work and maintenance, and when prompting, retrieval and a good eval set get you there faster.
Building an AI sales assistant that reps keep using
AI · Feb 24, 2026 · 5 min
Building an AI sales assistant that reps keep using
Why most sales copilots are abandoned within a month, and the design choices that made one stick with a 40-person sales team.
Voice agents on the phone line: latency, handoffs and honesty
AI · Dec 09, 2025 · 5 min
Voice agents on the phone line: latency, handoffs and honesty
What it takes to put an AI voice agent on a real phone line, from sub-second response times to clean transfers and clear disclosure.
Giving AI agents tool access without handing over the keys
AI · Oct 15, 2025 · 5 min
Giving AI agents tool access without handing over the keys
Scoped credentials, server-side policy, confirmation steps and prompt-injection defenses for agents that can take real actions in your systems.
When workflow automation beats an AI agent, and when it does not
AI · May 27, 2025 · 5 min
When workflow automation beats an AI agent, and when it does not
A decision framework for choosing between deterministic automation, a single LLM step and a full agent, with cost and reliability trade-offs.
Automating invoice intake with LLMs without losing the audit trail
AI · Mar 19, 2025 · 5 min
Automating invoice intake with LLMs without losing the audit trail
A document-processing pipeline that extracts invoice data reliably, flags what it is unsure about, and keeps finance and auditors comfortable.
Write the evals before the prompt: testing LLM features properly
AI · Jan 14, 2025 · 5 min
Write the evals before the prompt: testing LLM features properly
How we build evaluation sets, graders and CI gates for LLM features so that every prompt or model change is measured instead of guessed.
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