Agents Overview
An AI agent is software that uses a language model to decide what to do next, then takes action against real systems on your behalf. That distinction—deciding and doing—is what separates an agent from a chatbot, an automation, or a static script.
This section is the playbook for designing, governing, and operating those agents in production.
Chatbot vs. Workflow vs. Agent
These terms get used interchangeably. They are not the same thing.
| Pattern | What it does | Decision logic | Risk profile | Example |
|---|---|---|---|---|
| Chatbot | Answers questions in a conversation. Read-only. | The LLM picks the words. | Low — outputs are text. | A support bot that explains your refund policy. |
| Workflow / Automation | Executes a fixed sequence (Zapier-style). | Hard-coded if/then. | Low to medium — predictable steps. | "When form submitted, create CRM record, notify Slack." |
| Agent | Chooses tools, calls APIs, and takes multi-step action. | The LLM picks the tools and the order. | Medium to high — can mutate data, send messages, spend money. | "Reconcile this invoice against our PO system, flag mismatches, draft an email to the vendor." |
If you do not need the LLM to choose tools or sequence steps, build a workflow instead. Agents are more flexible and more expensive — both in cost and in failure modes.
When to use an agent
Pick an agent when all of the following are true:
- The path is not known in advance — the user's request determines which tools fire.
- The work spans more than one system or step.
- Some judgment is required (classify, summarize, decide).
- The cost of a wrong action is bounded — either by tool permissions, by HITL approval, or by reversibility.
If you can answer "no" to any of these, prefer a Zapier-style workflow or a chatbot — they will be cheaper, faster, and easier to debug.
How to read this section
The pages below are ordered the way most teams encounter the work:
| # | Page | When you need it |
|---|---|---|
| 2 | AI Agent Configuration Framework | Configuring a Custom GPT, Gem, or in-app assistant. |
| 3 | Role, Goal, Backstory | Writing strict instructions for a production agent that calls tools. |
| 4 | Agent Architecture Standards | Designing the system: modularity, integration, POC → production. |
| 5 | Agent Governance & HITL | Approval flow, operating rules, observability before launch. |
| 6 | Agent Lifecycle | End-to-end map from idea to ongoing operation. |
| 7 | Agent Evaluation Framework | Deciding whether the agent is good enough to ship. |
| 8 | Agent Security | Prompt injection, PII, secret hygiene, tool permissions. |
| 9 | MCP Intro | What Model Context Protocol is and why it matters for agents. |
Where this fits in the rest of the site
- Executive AI Glossary — definitions for
LLM,Agent,RAG,Token. - Modern AI Tech Stack — the four-layer reference architecture agents sit on top of.
- Hallucination Prevention Protocol — data-side defenses that complement agent-side guardrails.
- AI Policy Framework — organization-wide policy that any agent must comply with.
Need help implementing or feeling stuck? Contact us today to establish a consulting relationship.