Agent overview — the three axes of Tool · Actor · Intent

In D.Hub, an agent is a workflow that takes natural-language input and runs a full cycle of tool calls + decisions automatically. This lesson pins down the three axes of an agent and the 5-step one-cycle flow.

The three axes

1. Tool

A tool represents read · search actions. A call that fetches external data without side effects.

Examples:

• get_enriched_order(order_id) — Fetch one row from a dataset
• search_refund_policy(query) — Embedding search over a knowledge resource (RAG)
• query_tactical_zone(zone_name) — Look up tactical zone metadata

A tool is safe to call multiple times. Same input, same output — deterministic reads.

2. Actor

An actor represents write · action operations. Calls that change external state.

Examples:

• issue_refund(order_id, amount) — Refund API call, an actual financial transaction
• send_rejection_email(customer_id, reason) — Email send
• log_map_event(user_id, command, output) — Audit log write
• capture_review_decision(reviewer_id, decision) — An actor that takes a human reviewer's decision (HITL)

Actor calls can't be undone. An email fired can't be recalled, and a refund API creates a completed transaction. Almost every agent safety pattern concentrates on gates immediately before the actor call.

3. Intent classification

Intent classification is an LLM call that classifies the input itself to route it to the right flow. Before calling any tool or actor, it maps the user's input to one of the allowed categories.

Example (map_control's classify_intent):

• UI_CONTROL — Screen control intent → allowed
• DATA_QUERY — Data query intent → blocked
• SYSTEM_CONFIG — System config change intent → blocked

Intent classification is default deny (Lesson 5).

The 5-step one-cycle flow

The three axes combine into a one-cycle flow:

1. User input → intent classification — Is this input within our agent's scope? One classification LLM call.
2. Tool calls × 1–N — Gather context. Dataset fetches · RAG search · external API queries.
3. LLM reasoning — With the gathered context, produce a recommendation or response. Citation grounding in the body.
4. (HITL) human review — A human actor accepts or overrides the recommendation. Only for flows with high safety risk.
5. Actor calls × 1–N — Apply the decision to external state. Fire the refund · send the email · transmit the coordinate JSON.

Each step holds a separated responsibility — so that an LLM malfunction in one step can be caught at the next step's gate.

Reference scenarios — three agent patterns

The 6 lessons of this Path take turns referencing three scenario patterns.

If you have the three scenario zips on hand, each lesson's example works immediately. Zip download + import follows the Essentials Path Lesson 5 pattern.

What you should be able to do after this lesson

• The three-axis division — Tool · Actor · Intent classification
• The 5-step one-cycle flow and each step's responsibility
• The first safety rule — actor calls cannot be undone

Next lesson

Define one tool with an input/output schema contract.