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What AI Agents Actually Do

· AgentsHow-ToFoundations

If you’ve tried a chat assistant, you’ve already touched a piece of what people call an AI agent. An agent is software that takes a goal, makes a plan, and uses tools—like databases, spreadsheets, APIs, or document parsers—to carry out that plan. Think of it as a reliable helper that follows the steps you’d give an intern: “look this up, fill that form, draft a summary.” The difference is that an agent can act directly on systems you approve.

TL;DR

  • Agents are tool users. Give them explicit tools and clear goals.
  • They follow a loop: plan → act (call tools) → check → repeat → deliver.
  • Start small: low-risk, repetitive workflows; keep permissions tight; log everything.

Agents vs. Chatbots

Chatbots mainly produce text. Agents take actions. Ask a chatbot for enrollment trends and you’ll get a paragraph. Ask an agent (with access) and it will query the data source, calculate deltas, render charts, draft a narrative, and save the package where your team expects it. The text explains the work; the agent does the work.

The Three Core Ingredients

  1. Goal: the outcome you want (e.g., compile this report for last month).
  2. Tools: actions the agent is allowed to take (query DB, read PDF, call CRM API).
  3. Feedback: checks and validations to confirm each step was useful and safe.

The Agent Loop (Plan → Act → Check)

Most productive agents follow a simple pattern:

  1. Understand the goal. Specific beats vague. “Summarize last month’s support tickets by category and propose three fixes” is ideal.
  2. Break into steps. Collect data → clean → analyze → visualize → draft → file.
  3. Pick tools for each step. Adapters to data, utilities for parsing, libraries for charts.
  4. Run a step and capture outputs. Prefer structured outputs (tables/JSON) over free text.
  5. Check the result. Empty tables? Wrong date range? Missing fields? Fail fast, fix early.
  6. Iterate. Update the plan as new information appears; repeat until success criteria are met.
  7. Package the outcome. Deliver files, links, and a clear cover note; store them predictably.

Tip: A run log (plan, tools called, key outputs, checks) turns agents from black boxes into teachable coworkers.

Picking & Scoping Tools

Tools are explicit and permissioned. Good setups list each tool’s name, capabilities (e.g., get_record, list, create), inputs, outputs, and guardrails (read-only? rate-limited?). Agents can only act where you grant access—this is a feature, not a limitation.

Why Structured Outputs Matter

When a tool returns a table or JSON, downstream steps—joins, filters, charts—are dependable. Unstructured text is fine for drafting, but structure is king for operations.

Safety, Privacy, and Audit

  • Least privilege: Only the keys the agent needs.
  • Read-only first: Start with safe access; add writes later with approvals.
  • Audit trails: Log who/what/when/why for every tool call.
  • Boundaries: Human approval for sensitive actions (e.g., external emails, financial changes).
  • Retention & review: Keep run logs, spot-check results, rotate credentials.

What Can Go Wrong (and How to Avoid It)

  • Tool mismatch: The agent queries the wrong source or filters incorrectly.
    Fix: Clear adapters, sample queries, and simple validations (“date range must match request”).
  • Vague objectives: “Make things better” yields busywork.
    Fix: Write a single-sentence goal with a success test (“Draft a 1-page summary with 2 charts and 3 recommendations”).
  • Hallucinations: Confident text unsupported by data.
    Fix: Fetch facts via tools, cite sources, link datasets/calculations in the output.

Two Relatable Examples

1) Monthly Operations Brief

Human process: Pull metrics from two systems → check missing values → chart → write summary → file.

Agent: Run saved queries → validate date ranges → compute WoW/MoM deltas → render charts → draft narrative → store PDF + source tables in the team folder. A person skims and tweaks.

2) Triage Common Questions

Agent: Classify incoming message (billing/password/enrollment hold) → fetch policy snippet → draft reply. For low-risk classes (password), include step-by-step guide; for sensitive topics (financial decisions), leave sending to a human. Clear handoffs build trust.

When to Use an Agent (and When Not To)

  • Use an agent when the workflow is repetitive, slightly variable, and verifiable. Think “if A, do X; else draft request for B.”
  • Use a scheduled report/integration when the task is identical every time.
  • Avoid full automation when stakes are high, ambiguity is large, and results can’t be verified automatically—keep humans in the loop.

Getting Started: A One-Week Plan

  1. Day 1: Pick a low-risk workflow and write a one-sentence goal.
  2. Day 2: List required tools and confirm read-only access works.
  3. Day 3: Map 3–5 steps and define “done” (artifact + checks).
  4. Day 4: Dry run. Did validations catch anything? Adjust.
  5. Day 5: Run with a real request. Compare against the human baseline. If it saves an hour, you’re on track.

Measuring Value

  • Speed: time-to-complete; turnaround for approvals.
  • Quality: error rate; rework; missing-data flags.
  • Experience: wait times; satisfaction on responses; first-contact resolution.

Skills vs. Tools

Think of tools as external actions (query a system, read a file) and skills as reusable reasoning patterns (categorize emails, write a two-paragraph summary, find anomalies). Agents combine both. A single workflow might use the “categorize” skill, then the “query” tool, then the “summarize” skill.

What a Good Run Log Looks Like

Plan:
  1) Query tickets last 30 days
  2) Group by category; compute MoM change
  3) Render 2 charts; draft 3 recommendations
Tools:
  tickets.list(range=2025-08-01..2025-08-31)
  charts.render(bar, line)
Checks:
  - Non-empty table
  - Dates within range
  - Top category change >= |5%|
Output:
  /reports/2025-08/ops-brief.pdf
  /reports/2025-08/ops-brief-data.csv

Compliance Layers to Keep in Mind

  1. Purpose: What business need justifies access?
  2. Permissions: Who/what can the agent read/write?
  3. Logging: What’s recorded for every action?
  4. Retention: How long are logs/artifacts kept?
  5. Review: Who spot-checks results and how often?

Quick FAQ

Do agents replace people? No—they shift people to judgment, exceptions, and relationships. Agents take the repetitive glue work.

How accurate are agents? Accuracy tracks with data quality, tool reliability, and validations. Add checks; don’t rely on vibes.

What’s the hardest part? Choosing a first use case with clear success criteria.

Glossary

  • Agent: Software that plans and acts with tools.
  • Tool: A permitted action on a system (query, read, write).
  • Adapter: The safe connection layer to a system.
  • Skill: A reusable reasoning pattern.
  • Validation: A rule that must be true (e.g., dates in range).
  • Run log: A record of plan, actions, outputs, and checks.
  • Human-in-the-loop: People approve or edit key steps.

If you remember one idea, remember this: agents are structured doers. They turn clear goals into checked steps using approved tools, with visible logs. The technology is impressive, but the wins come from simple habits—good goals, small pilots, tight permissions, and clear handoffs. Start there, and agents become an everyday productivity boost rather than a science project.