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What Is an Agent?

An Agent is an autonomous, goal‑driven AI assistant that can understand a request, decide the next best step, run one or more Actions, and return a grounded, useful result. Unlike a single prompt (which just predicts text), an Agent performs a mini workflow: classify intent into a Topic, plan or ask clarifying questions, invoke Actions securely, iterate, validate, and respond.

Mental model: Message → Topic → Reason / Act Loop → Validate → Response.

Core Capabilities​

  • Interprets free‑form natural language (not just buttons / rigid menus).
  • Chains tasks: gather data, transform, update, summarize, draft content.
  • Maintains short recent conversation context for coherent follow‑ups.
  • Applies guardrails: scope limits, confirmation steps, identity checks.
  • Improves over time as you refine Topics, Actions, and instructions.

Building Blocks​

  1. Agent – Container (persona, collection of Topics, config).
  2. Topics – Job definitions: when to engage, boundaries, rules, allowed Actions.
  3. Actions – Executable units (API call, function, workflow, data lookup, generation) the Agent can run inside a Topic.
  4. Reasoning Engine – Orchestrates classification, tool choice, multi‑step loops.
  5. LLM – Language reasoning core used for understanding, decision steps, and drafting responses.

If any piece is vague (especially Topic scope), reliability drops—tighten definitions first instead of adding more features.

How a Message Flows​

  1. Input – User (or system trigger) sends a question / command.
  2. Topic Selection – Recent turns + each Topic’s name & classification description are compared; best fit chosen (or off‑topic fallback if none match).
  3. Reasoning Loop (repeat up to a small cap):
    • Decide: run an Action, ask for missing info, clarify ambiguity, or answer directly.
    • If an Action runs: execute → map outputs → append to context.
    • Check if goal satisfied; else iterate.
  4. Validation – (Optional) ensure response is in‑scope, grounded, policy‑safe. Regenerate or escalate if it fails.
  5. Response – Stream or send the final answer and wait for the next turn.

Lever: precise Topic instructions strongly shape step 3 (what to ask first, which Action to prioritize, when to stop).

Agents vs Scripted Bots​

AspectAgentScripted Bot
Conversation styleAdaptive, free‑formPredefined flows / branches
Intent handlingSemantic (LLM classification)Rules, keywords, narrow model
Tool useDynamic, multi‑Action plansUsually fixed single flow
AdaptationAdjust by refining Topics/ActionsManual flow editing
Best forVaried, messy tasksDeterministic, regulated steps

Choose a scripted bot when strict, auditable, unchanging flows dominate. Choose an agent when variability, blended context, and multi‑step reasoning matter.

Common Use Cases​

  • Resolve support inquiries end‑to‑end (lookup → policy check → answer).
  • Summarize records (case, ticket, lead) + propose next actions.
  • Draft outbound messages (email, notification, follow‑up).
  • Orchestrate data workflows (fetch → validate → update → summarize result).
  • Internal analyst: explain KPIs, highlight anomalies, surface risks.

Start narrow (one high‑value task). Broad first scopes cause misclassification noise.

New Topic or New Action?​

Create a new Topic when: intent category or decision rules differ meaningfully, or a current Topic would need many conditional branches. Create a new Action when: same intent category but you need a different external operation / side effect.

Avoid: catch‑all Topics, mega Actions doing unrelated jobs—they reduce transparency & testability.

Narrative Example​

A "Support Assistant" has Topics: Password Reset, Order Status, Return Eligibility.

Step-by-step flow:

  1. User: "Where's my package?"
  2. Classifier picks Order Status Topic
  3. Instructions require order_number or identifying email fragment
  4. Agent asks if missing information needed
  5. Runs LookupOrderStatus Action
  6. Normalizes carrier + ETA data
  7. Replies with concise status
  8. Offers follow-up ("Need to place a hold or start a return?")

Next Steps​

Ship a small, well‑bounded agent; observe interactions; iteratively tighten definitions. Precision wins over breadth early on.