What Are AI Agents?

Most people use AI like a search engine — type a question, get an answer. That's a chatbot. An AI agent is fundamentally different: it has goals, it can use tools to interact with the real world, it can detect and fix its own errors, and it can coordinate multi-step workflows without you guiding every step. The shift from chatbot to agent is the shift from asking to delegating.

Peter Saddington's framework for designing production AI agents. Every effective agent has exactly four components:

1. Personality

The agent's identity, persona, and communication style. This isn't cosmetic — personality determines how the agent interprets ambiguity, handles edge cases, and interacts with humans. A financial analyst agent communicates differently than a creative copywriter agent, and that difference affects output quality.

2. Goals

What success looks like, defined as measurable outcomes. An agent without clear goals is just a chatbot with extra steps. Goals constrain behavior, prioritize actions, and provide the evaluation criteria for self-correction. Good goals are specific, observable, and testable.

3. Tools

Individual actions the agent can take: read a file, send an email, query a database, call an API, browse the web. Tools are the bridge between AI reasoning and real-world impact. Without tools, an agent can only think — with tools, it can act.

4. Skills

Multi-step workflows that chain tools together. "Read a file" is a tool. "Deploy a website" is a skill — it chains read, build, deploy, and verify into a coordinated sequence. Skills are the choreography that turns individual actions into meaningful outcomes. The distinction between tools and skills is the key insight that separates toy agents from production systems.

These six capabilities form the spectrum from basic AI to truly agentic systems. A chatbot has reasoning only. A production agent has all six. Peter's AI Workshop dedicates an entire module to each capability and culminates in building one agent per capability — six working agents by the end.

Peter doesn't just teach agent theory — he runs production agent systems. Here are real examples from his network:

pRAG — Personal Retrieval AI

A conversational AI at staas.fund trained on 14,500+ chunks of Peter's actual content — talks, books, YouTube transcripts, Substack posts, training materials. Uses Supabase vector search for retrieval and a 5-provider LLM failover chain (Groq, Gemini, Cerebras, SambaNova, Cloudflare Workers AI) for generation. Rate-limited, CORS-protected, and serving thousands of queries.

4-Agent AI Council

An autonomous multi-agent system with four specialized agents — HH Platform (infrastructure), Nyx Security (security monitoring), MiniDoge Business (business intelligence), and Saarvis Network (network coordination). These agents monitor a 10-site network, generate daily reports, detect anomalies, and coordinate responses — running on automated schedules without human intervention.

Agent Library — 50+ Templates

A library of 50+ production-ready agent templates spanning 12 departments (Engineering, Marketing, Sales, HR, Finance, Legal, Operations, Support, Product, Design, Data, Executive) with 20 industry-specific playbooks and step-by-step building guides. Each template includes a complete system prompt, defined goals, tool requirements, and skill definitions.