A beginner's introduction to agentic AIRevised October 2026

Agentic
AI.

How Claude Code, Codex & co. actually work

model → harness → agent ↺
2
The hook

You type one line. It does the rest.

you › "the invoice test is failing — fix it" …then, with no further input, the AI on its own: ✓ ran the tests saw it expected 121, got 100 ✓ opened invoice.py spotted VAT was never applied ✓ edited the code added the 21% VAT line ✓ ran the tests again PASS ✓

Illustrative trace: a passing test is evidence for that case. Review the diff and relevant edge cases before accepting the change.

3
Core · chatbot vs agent

A chatbot answers. An agent acts.

Chatbot — the one you know

You ask → it tells you what to do → you open the files, run the commands, paste the errors back… and repeat.

Agent

You ask → it does the work itself — reads the files, runs the commands, checks the result → you review at the end.

The model proposes actions. The harness executes permitted tools. Together, repeating this process, they form an agent.

4
Our route

Give an agent a task you can check

Understand the loop

Who chooses an action, who runs it, and how the result comes back.

Guide the work

Supply relevant context, useful project rules and scoped permissions.

Check the result

Inspect the changed files and run the check that defines done.

One example throughout: an invoice total that is missing VAT.

5
Harness · what runs where

Model in the cloud, harness on your machine

YOU you talk → chat AGENT = harness + model, in a loop YOUR MACHINE (local) CLAUDE CODE the harness your files ▸ terminal runs actions CLOUD MODEL Claude model stateless — no memory sends context → ← next action

You talk to the agent. Claude Code is the harness — it runs locally with your files & terminal, and loops with the model in the cloud (which stays stateless). The agent isn't a separate thing — it's that loop.

6
Model · example

"No memory" in practice

There's no "conversation" on the model's side. Each call hands it the whole history as one flat list:

# What the model is handed on turn 3 — the WHOLE chat, again: you said: "My name is David." it replied: "Nice to meet you, David." you now ask: "What's my name?" ← answerable only because line 1 was re-sent

Delete the first line and the model can't answer. The "memory" lives in the request — not the model.

7
The request

One message sits inside a larger request

SYSTEM role, rules and tool descriptions PROJECT how to run the invoice tests HISTORY the task and earlier results TOOL OUTPUT invoice.py and the failing test YOUR MESSAGE "fix the missing VAT"

Context is the information supplied for this call. Tokens are the chunks used to measure it.

8
Optional live example

Watch one bounded task

Fix the invoice calculation so the VAT test passes. Do not change the test or unrelated files. Run the relevant tests and show the final diff.

Optional live run: use a disposable project and scoped permissions. The next slide provides an illustrative trace if no demo is available.

9
Agent · the loop

An agent is a loop

  • Propose: the model chooses the next action.
  • Run: the harness executes it under the configured permissions.
  • Observe: the result returns as context for the next step.
  • Check: stop when the task is verified, or ask when blocked.

In a live run or a finished transcript, point to one tool call and its result. That is one lap of the loop.

10
The loop

The invoice fix, step by step

run tests → FAIL: expected 121, got 100 read invoice → VAT is missing from the total edit code → apply the order’s 21% VAT rate run tests → PASS review diff → check scope and edge cases

Illustrative trace. The model requests the actions; the harness runs them and returns the results.

11
Thinking · how it works

The model thinks before it answers

PROMPT ILLUSTRATIVE REASONING • recall the VAT rule (21%) • 100 × 1.21 = 121 • so the test expects the tax added (these reasoning tokens cost money too) ANSWER
  • Reasoning gives the model room to work through intermediate steps before answering or acting.
  • More effort can help a difficult diagnosis; it also takes more time and tokens.
  • For the invoice task, verify the VAT rule and run the test. A plausible explanation alone is not evidence.

Visible thinking may be a summary. Judge the result and the checks, not how convincing the narration sounds.

12
Getting started

Choose the interface you already work in

Terminal

Run a coding agent beside your commands and tests.

Editor

Review and change code inside your existing development workflow.

App or web

Give a task, inspect progress, and review the returned files.

Look for clear permissions, visible changes and a way to run checks. Model and product lists are in the reference deck.

13
Getting started

Two ways to pay for the work

Subscription

A plan for using an app or coding tool, subject to its usage limits.

Metered API

Usage-based billing for integrations and automated workloads. Set a budget and monitor it.

Check the chosen product’s plan, supported access route and limits. Price per token is only one part of the cost of a completed task.

14
Context · what it is

Context = the model's short-term memory

  • Context contains the instructions, conversation and tool results supplied for this call.
  • The context window is the maximum capacity. Its size depends on the model and configuration.
  • Supply the relevant test and invoice code. An unread file cannot guide the model through this request.
CONTEXT WINDOW · finite capacity system prompt CLAUDE.md · rules conversation history files you opened tool output free space
15
Context

More context is not always more useful

Useful context

The failing test, the VAT rule and the code path that computes the total.

Distracting context

Old tasks, duplicate file dumps and unrelated logs that obscure those facts.

Watch for ignored requirements, repeated work and missed evidence. There is no universal percentage at which quality fails.

16
Context

Choose the next step from the task state

New task

Start a fresh conversation when the previous task is complete.

Same task

Summarize the goal, decisions, failures and next check before continuing.

Broad search

Delegate investigation and request findings with file references.

Inspect the context display as a capacity indicator. Check behavior and results before deciding what to remove.

17
Context

Cheaper reuse and shorter context

Caching

Reuses computed state for a matching prefix. It can reduce processing cost and latency; the content is still input.

Compaction

Replaces older material with a summary. It frees room, but details omitted from that summary may be lost.

For the invoice task, preserve the VAT rule, failing test, attempted changes and verification command.

18
Context · sub-agents

Sub-agents keep the main context clean

  • Delegate a focused investigation, such as finding every place invoice totals are calculated.
  • The helper’s intermediate tool output stays in its own working context. Ask for a concise result with evidence.
  • A fresh subagent needs a self-contained task. A conversation fork can inherit the existing history.
19
Permissions

Decide what the agent may touch

Scope the work

Allow the project files and test commands needed for the task.

Keep approval points

Ask before destructive actions, access changes or publishing outside the task.

Contain mistakes

For unattended experiments, restrict filesystem, network and credentials in an actual sandbox.

A separate folder or Git branch protects neither credentials nor the rest of the machine. Check the configured boundary.

20
Project knowledge

Teach it the rules it cannot infer

# CLAUDE.md — invoice project - Run invoice checks with: python -m unittest - VAT rates come from the order record. - Do not replace the tests to make a fix pass. - Ask before changing the stored invoice schema.

Keep standing rules short. Use the instruction-file convention supported by your harness, such as CLAUDE.md or AGENTS.md.

21
Project knowledge

Save a repeatable check as a skill

invoice-check/ SKILL.md when to run + how to verify scripts/ optional deterministic checks references/ VAT and rounding rules

Skill: a reusable procedure loaded when relevant. Plugin: a way to distribute a toolkit to a team.

22
External tools

Give the agent access to the next fact

CLI

Run the project’s invoice tests and inspect Git changes.

API

Fetch a permitted record through a service interface.

MCP connection

Expose supported issue-tracker or document tools to the agent.

Choose the narrowest useful access. A connector does not grant permission to publish or change unrelated records.

23
Reaching out · MCP

MCP = "USB-C for agents"

without MCP — N×M glue with MCP — connect once MCP
  • MCP standardizes how a supported client discovers and calls tools exposed by a server.
  • Example: read the issue describing the invoice bug, then propose an update with verification evidence.
  • Each integration still needs compatible support, authentication and scoped permissions.
24
Try this next

Give a bounded task. Check the result.

  • Goal: name the result and the files or system in scope.
  • Context: supply the relevant examples, rules and failure evidence.
  • Permissions: decide what can run and where approval is needed.
  • Acceptance: inspect the diff, run the relevant check, record what remains unverified.

Optional reference: terminology, products and historical pricing · All talks