{ "cells": [ { "cell_type": "markdown", "id": "678da23b33585c2d", "metadata": {}, "source": [ "# Chapter 3, Unit B: Repair failed-call accounting\n", "\n", "> **Learn with Prof Rod** — *Build Your Always-On AI Agent From Scratch*.\n", "> **Read the full book and get the latest learning materials:** [https://profrod.ai/book](https://profrod.ai/book).\n", "> **Join the Prof Rod learner community:** [https://profrod.ai/community](https://profrod.ai/community)\n", "> — bring your questions, compare experiments and share what you build.\n", "> **Original source and updates:** [profrodai/sovereign-agent](https://github.com/profrodai/sovereign-agent).\n", "\n", "**Student edition · 90 minutes of dedicated work · 2026-09-09**\n", "\n", "This is one of two practical units for Chapter 3. Unit A constructs and connects the\n", "mechanism; Unit B investigates a controlled failure, repairs it and transfers the invariant.\n", "Each is a complete ninety-minute session, with its own setup and required conceptual introductions.\n", "Basic Python variables, conditions, loops, functions, lists and dictionaries are the starting\n", "knowledge. Libraries and specialized concepts used here are introduced below before the main task.\n", "\n", "By the end you should be able to:\n", "\n", "1. Explain the chapter's mechanism using a prediction and an observed intermediate result.\n", "2. Repair failed-attempt exposure accounting and distinguish repeated tool-call IDs from exhausted provider attempts.\n", "3. Solve **admit a variable-cost next model attempt** using changed inputs and an independent expectation.\n", "4. Retain your implementation, failed/corrected observations, causal explanation and limits.\n", "\n", "| Minutes | Dedicated activity | Evidence you produce |\n", "|---|---|---|\n", "| 0–5 | State the problem and make a prediction | Initial prediction in your own words |\n", "| 5–25 | Foundations and library examples | Values, explanations, revised predictions |\n", "| 25–35 | Trace setup and the main interface | Input → learner function → observation |\n", "| 35–60 | Reproduce, diagnose and repair | Source, visible checks and runtime evidence |\n", "| 60–80 | Implement and challenge the transfer task | Function and a new counterexample |\n", "| 80–90 | Retrieve, explain and save | Exit ticket and retained submission |\n", "\n", "Installation is preparation time. These are planning estimates, not measured completion times.\n", "Use the reference primers when a term is unfamiliar; in Unit B retrieve an explanation before\n", "re-reading it. Run All checks that the artifact executes. Unfinished student functions deliberately\n", "produce NEEDS_WORK. Keep your first attempt before opening answers.\n", "\n", "\n", "This notebook belongs to the nineteen-chapter edition. Its supplied teaching runtime is embedded, so it can run without the textbook or another notebook. Where code uses `REFERENCE_LESSON`, that is the frozen runtime exercise identifier; the reader-facing chapter and saved unit identifiers use the current edition. Building against a supplied runtime is not proof that you have constructed all of its dependencies.\n" ] }, { "cell_type": "markdown", "id": "ba8dbc0d833a727a", "metadata": {}, "source": [ "## Run the self-contained setup\n", "\n", "Use a **Python 3.14** Jupyter kernel and **Pydantic 2**. If needed, run\n", "`%pip install \"pydantic==2.13.4\"` once in a separate cell and restart the kernel.\n", "Package installation needs internet; the lesson itself needs no repository download, API key\n", "or prior notebook. The complete source runtime uses Python 3.14, so this edition does not claim\n", "compatibility with a hosted notebook service's default interpreter.\n", "\n", "The collapsed cell contains 89 frozen teaching files. Base85 represents compressed bytes\n", "as text; `zlib` decompresses them; SHA-256 checks that the decoded files match this edition.\n", "These are supplied packaging operations, not learner algorithms. `tempfile` creates an isolated\n", "working copy; `Path` handles file locations; `sys.path` tells Python where the supplied modules\n", "live. The code is available for inspection below and performs no package installation itself.\n", "The subsequent lesson teaches the libraries used by the mechanisms you will implement.\n", "\n", "Run setup on every fresh kernel. It writes scratch runtime files separately from your retained\n", "`practical-work/ch03-b` folder. Rerunning setup restores the frozen support files and keeps\n", "your saved work. Restarting a kernel clears variables, not saved submission files. Source basis:\n", "Sovereign Agent `444c5f6`. Some tasks use reviewed local subprocesses; they are not an OS sandbox.\n", "\n", "
Supplied offline setup and teaching files\n" ] }, { "cell_type": "code", "execution_count": null, "id": "837e636395d62e33", "metadata": { "jupyter": { "source_hidden": true }, "tags": [ "setup", "embedded-runtime" ] }, "outputs": [], "source": [ "import base64\n", "import hashlib\n", "import json\n", "import os\n", "import sys\n", "import tempfile\n", "import zlib\n", "from pathlib import Path\n", "\n", "minimum_python = (3, 14)\n", "if sys.version_info[:2] < minimum_python:\n", " raise RuntimeError(\"Use a Python 3.14 kernel for this complete-book practical course.\")\n", "try:\n", " import pydantic\n", "except ImportError as error:\n", " raise RuntimeError('Run %pip install \"pydantic==2.13.4\", then restart the kernel.') from error\n", "if pydantic.__version__.split(\".\")[0] != \"2\":\n", " raise RuntimeError(\"Use Pydantic 2; the tested version is 2.13.4.\")\n", "\n", "# Frozen, reviewed course files: data until explicitly loaded by the lesson.\n", "COURSE_ARCHIVE = (\n", " \"c-ri}3v=5>)*$*<@Oi4P$%LXwJuP}Udlgxk_{OokmXhq0q+)>tC}B(y3_)7fxb)xed3B?^(EveN_SjRJZ#Nc\"\n", " \"!Mn6xV_v6!(VVb^w7EW%$+iZ|dpN+2K(fe7NOcxpa-RZVxw@-Rc&guUSufo|Po(J7tkWS+u{y3W?qht|Wr%^\"\n", " \"l!=J8+4I9s%Wn`Chngwr4$k0%MVo5$HKon~<`PChP{^SI5uolnoF$@MIqFM@D>ISc1mtRIZh*{%L@6=qkHWT\"\n", " \"=1tD@)qo^G8#aZb)S\"\n", " \"uJb3<6bGe0TGlH$eR^v_g4HZr%;WGH8~q$kverTGDzuIU1S*Xv9WAe6C)KKxF`ZwMCDUvXPDinB(F&quq}$OBg$bPSAK_#fAI<0Kyn*L(J)6XfB%K\"\n", " \"EF^aDW7EDo*#M*$SknGgR3Xb$$6I~iT2$tcbMp{YUIm+=C-;TO7LaaPiz6-)qrnh0H!c&eT@ga7FR;;)=35(\"\n", " \"Q(nh?M~cN6y?}YSQOf=c0NpvOfgq^gz}!PNqqAHQ*D1?s0Ai2k8tBsqSV*NkpS^XYl9zI-E>y^=t*=^mJuxd\"\n", " \"5m}F(_QIaN-JXl@AUgXwhwBnt_aObDI@KhhMK;$QeyQdmOX2oBudi{O9x_YB^e!>H*pxJ\"\n", " \"-E|dii?zt2q)$cn{D3w_^mC!l27@CX!6@=#N)Ne_9\"\n", " \"OQ`IBfKj%m~B(wlF_lkk%IDWV2zFC4B)<3u`114a5il5+!oG|NEN0`e6r5iDzX1TwYpe9-)AAe-38;>oy$Ti\"\n", " \"Oc70Kf>YfL*i8R&WJ77|(%6nZ`FD{H-`XHD*BcG>%>b@LxJXAVTBh61c($n5u{{PQY!215iLXNj}6xs2d2fY\"\n", " 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\"*q=SW8xD3l{#NwO2nU(j\"\n", " \"P4*0bKqpx~pJel7wL_+@=mijY~xu5jB*wS2-pGRiqrS#(CH`bsTzub_^8wbsgu%cj1)h-*r*R3$dd(Z6)cKKcvdg-Q;OyED@xqXn3w`?UDYl8UBCa+zG)F(`XS7^66;BKHn9*q?S)7EJukVb#99u0jFjz8k4uH_Q5?i9#qB3@6clC!CiWgRu5Mb#=fH1=P{TtVgWRIpQMD7P~vs5Kezy`QX`+Y9yzob=LZMv\"\n", " \"a|ws)C$%te4hCuVy#Ukyu8LEH`kR^QA~@=cL7i`DmhNOs9>%ad55aR}u;ete6cW?6X(W?ay^fxmBxsiST&Te\"\n", " \"+bUAV9(I$uhSKX!Cv8Iq=<9ivmIgKq|MPw;ikMb`F)yqcHhJu~SB9dh&p\"\n", " \"%u`yqG3a~O@4McpLP2G+#jIub*rjx(&^YR}9o_PU2SM8z=!cHZ!a)A*aDxN-e@7b~xW+yH>?T4G)oe7`NCS7\"\n", " \"LF2P7F6Y_hHHQY~;dAAao#4)9`n+bq)9N!8rAbV^aNp#hEa@8ulqPvB3*X)9kp!ZnpYzmMe;a(RjX9%?Y`1G\"\n", " \"A&R?Cd&Da>Ltxc3t8W;M_q3sdVBgTS0Q^C;-#bbaQLp+NLDab^3OHo)xS%CbG1OD?@owrqM#(?~;X#?Yk%j0\"\n", " \"09<%ka#`mN_R8moIXTfle-pq7<6woI_0@(cw{8&o$&j=$-_GgqU8C1R*&VjoBVV%IyaQ;VF_BD2JCgSBKN*I\"\n", " \"kbNYz=BMvc~1$NiEu5*k3$C;=gIVzm#|TFC%0S(eSKIr8P_?8PBS)Mv=`>ZkA{DKcRbwl;*q@iA+x&Mki?}_\"\n", " \"q$055B)oq6Wsq`o%9IYC(rXDik?>DB#LS`LY{)Hg<6gS1YjfQ(E+L~M8KkY)n6u?7U#-dFOX#iHB1~7\"\n", " \"6E-|y)T(V$F*1$M&t>IYAQMTd(SaLW_g{R51Z!`K=WOpU>rN9K=ivG7vPGz1K96Nh6hdARyutqiQ^lx@TmD2OO!vLoi$9|-xVr=quOCF\"\n", " \"~J%yRho>h@=7&bY<3fmjoclYFp27lbI=K#`js%7DdU@a-0l^+D+Ve^{\"\n", " \"N?K!|rKji3kDuVv?o=`fpc60BbIO>o1ZTcS$gZg1NYe$urvkPG@pW*bll4;Ja9wdFl9QRsv?OPUT`sb@>Qq5\"\n", " \"&%vu!b;Sq~R)@84ig1CCx*Y;t!cXO;H->ofe-|)ZREN|n{H@VF7zB|*%o(d9qr?{F+_p3+VyiB<)ryc8|EWC\"\n", " \"#pk%ynMZ1E5hLnkf99g8Iv!wW0a%&C@7pvcEUc=na6&WkgS-%&EgSwFdX*hwC8n!6~3^#O0UZ#M?=);!FkYD\"\n", " \"!{5K7bO%4ZQ5Ux^qj)!oBd=@1Zh|HhTA6o5kzbAdZ{dgyW8=FYVLPa!k^y$%wS)l=dj{H=HnN9M4Lqxx4iME\"\n", " \"+yLGZ(FkYaCQ?0vfiFk9%y^vy#+}*R!gX!k#>WwH|EYq*@$)5-F!^a`FKwm|L|acZjOspPFvuGIZW%^-E=S_\"\n", " \";mN+E=t4$Rme)cy%cO`KMBmPjC;Xgt70YlCTfl)Hs3EBbry8H0WSTA7Oj@fNFQz+sTGbp+oz#Z;GPB+qlOyj\"\n", " \"8|N4Dr7N}ZZHjs!T>o$6TcWS41_1F&R-nlJ;`{y5#zDW8fn>#q;C7@Loc#a*0O\n" ] }, { "cell_type": "markdown", "id": "06bcd00b59ee1ffb", "metadata": {}, "source": [ "## Commit to a prediction before the examples\n" ] }, { "cell_type": "code", "execution_count": null, "id": "48d003154dcf5dbe", "metadata": { "tags": [ "prediction", "learner-notes" ] }, "outputs": [], "source": [ "prediction_notes = {\n", " \"prediction\": \"Write the expected behavior before running the worked example.\",\n", " \"reason\": \"Name the input and rule behind that prediction.\",\n", " \"falsifier\": \"Name an observation that would prove the explanation wrong.\",\n", " \"revision\": \"After execution, explain what changed in your understanding.\",\n", "}" ] }, { "cell_type": "markdown", "id": "a438cd492efc66d8", "metadata": {}, "source": [ "### Reading the Python vocabulary used in this notebook\n", "\n", "You need basic assignments, `if`, loops, functions, lists and dictionaries. The less familiar\n", "features used by the supplied code are introduced here. A **library** is reusable code that\n", "Python can import. The **standard library** ships with Python; Pydantic is an additional package.\n", "An import makes a name available, but does not mean that you have completed the exercise.\n", "\n", "**JSON** is text for exchanging structured values. A Python dictionary is an in-memory object;\n", "the JSON representation is a string. Use `json.dumps` to encode and `json.loads` to decode.\n", "Decoding proves that text has valid JSON syntax, not that its fields match our business contract.\n", "Predict which of the following two decoded objects could describe a stock count." ] }, { "cell_type": "code", "execution_count": null, "id": "3e6766277a2a1e6b", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "import json\n", "\n", "intro_data = {\"sku\": \"MANGO\", \"count\": 3}\n", "intro_text = json.dumps(intro_data, sort_keys=True)\n", "print(type(intro_data).__name__, type(intro_text).__name__, intro_text)\n", "print(json.loads(intro_text))\n", "print(\"Also valid JSON:\", json.loads('[\"not\", \"a\", \"stock\", \"record\"]'))\n", "assert json.loads(intro_text) == intro_data" ] }, { "cell_type": "markdown", "id": "c47a7afde4535294", "metadata": {}, "source": [ "The first result is a dictionary; the second is a list. Before indexing a decoded object,\n", "check the shape that your function promises to accept. An **exception** interrupts the normal\n", "path. `raise ValueError(...)` refuses an invalid value; `try`/`except` lets a caller inspect that\n", "expected refusal. Catch the expected class, rather than turning every programming error into\n", "apparent success. `finally` runs cleanup even when an earlier operation raises.\n", "\n", "An **annotation**, such as `count: int`, documents the expected type. It does not by itself\n", "enforce the type at runtime. A **class** defines a kind of object; an instance holds one object's\n", "data. `@dataclass` asks Python to generate routine construction and comparison methods from\n", "annotated fields. `frozen=True` prevents ordinary reassignment of the instance's fields; it does\n", "not make every object nested inside those fields immutable. A **method** is a function attached\n", "to a class; `self` refers to the instance receiving the call." ] }, { "cell_type": "code", "execution_count": null, "id": "c0afe2203f5f7cc6", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "from dataclasses import dataclass\n", "\n", "\n", "@dataclass(frozen=True)\n", "class IntroObservation:\n", " operation: str\n", " count: int\n", "\n", "\n", "intro_observation = IntroObservation(\"count-mango\", 3)\n", "print(intro_observation.operation, intro_observation.count)\n", "assert intro_observation == IntroObservation(\"count-mango\", 3)" ] }, { "cell_type": "markdown", "id": "2a3d8ba17346df34", "metadata": {}, "source": [ "A **callback** is a function passed to another function. This is how the classroom harness\n", "invokes *your* implementation. The argument `candidate` below is a function object; parentheses\n", "perform the call. Predict the two answers before execution, then trace the result to the callback." ] }, { "cell_type": "code", "execution_count": null, "id": "ea9e12f9b36f8001", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "def intro_apply(candidate, value):\n", " return {\"input\": value, \"observed\": candidate(value)}\n", "\n", "\n", "def intro_double(value):\n", " return value * 2\n", "\n", "\n", "print(intro_apply(intro_double, 3))\n", "print(intro_apply(lambda value: value + 2, 3))\n", "assert intro_apply(intro_double, 3)[\"observed\"] == 6" ] }, { "cell_type": "markdown", "id": "f9162bbd48ca9e6f", "metadata": {}, "source": [ "`lambda value: value + 2` is a small anonymous function. A **closure** is a function that retains\n", "access to values from its surrounding scope. It can bind a tool to a shop snapshot. A shallow\n", "copy duplicates only the outer container; `copy.deepcopy` also copies nested containers used\n", "in these fixtures. A **set** stores distinct values; `required <= allowed` asks whether every\n", "required item is allowed. `frozenset` is the corresponding immutable set. A tuple groups ordered\n", "values; `(value,)` is a one-item tuple, including the comma.\n", "\n", "**Paths and cleanup.** `Path` represents a filesystem location. `path / \"file.json\"` constructs\n", "a child path; `read_text` and `write_text` read and write text. A context manager, used with\n", "`with`, manages entry and exit. A temporary-directory context removes its contents on exit.\n", "Save your submission outside temporary runtime directories. Reopening a file is different from\n", "reusing a Python variable: the former tests retained bytes, while the latter only tests this kernel." ] }, { "cell_type": "code", "execution_count": null, "id": "cb18c20e825c3621", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "from pathlib import Path\n", "from tempfile import TemporaryDirectory\n", "\n", "with TemporaryDirectory() as intro_folder:\n", " intro_path = Path(intro_folder) / \"observation.json\"\n", " intro_path.write_text(json.dumps(intro_data), encoding=\"utf-8\")\n", " intro_reopened = json.loads(intro_path.read_text(encoding=\"utf-8\"))\n", " assert intro_reopened == intro_data\n", " print(\"Read from a file:\", intro_reopened)" ] }, { "cell_type": "markdown", "id": "f1f6b295ec41269c", "metadata": {}, "source": [ "**Retrieval check:** explain JSON versus a dictionary, annotation versus validation, class versus\n", "instance, and defining a callback versus invoking it. Change the callback above so an incorrect\n", "implementation visibly changes the observed output. This distinction will matter when grading\n", "your connected work. Reference: Python's [JSON](https://docs.python.org/3.14/library/json.html),\n", "[dataclasses](https://docs.python.org/3.14/library/dataclasses.html), and\n", "[pathlib](https://docs.python.org/3.14/library/pathlib.html) documentation." ] }, { "cell_type": "markdown", "id": "902841a8562b2669", "metadata": {}, "source": [ "### Pydantic: turn an input dictionary into a checked object\n", "\n", "Pydantic is an additional Python library for validating data. Its **model** is a class describing\n", "fields, not a neural network. Inherit from `BaseModel`, declare annotated fields, then call\n", "`model_validate` on incoming data. A field without a default is required. A field with a default\n", "can be omitted. The result is an instance whose values you read with dot notation." ] }, { "cell_type": "code", "execution_count": null, "id": "801f6ef5c6bc3a58", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "from pydantic import BaseModel, ConfigDict, Field, ValidationError\n", "\n", "\n", "class IntroCourseRequest(BaseModel):\n", " model_config = ConfigDict(strict=True, extra=\"forbid\")\n", " name: str = Field(min_length=1)\n", " quantity: int = Field(gt=0, le=1000)\n", " note: str = \"\"\n", "\n", "\n", "intro_request = IntroCourseRequest.model_validate({\"name\": \"mango\", \"quantity\": 4})\n", "print(intro_request.name, intro_request.quantity, repr(intro_request.note))\n", "assert intro_request.note == \"\"" ] }, { "cell_type": "markdown", "id": "95f1b81167ce5391", "metadata": {}, "source": [ "The annotation says the field's type. `Field` supplies constraints: `gt=0` means greater than\n", "zero, `le=1000` means at most 1000, and `min_length=1` excludes an empty name. `ConfigDict` sets\n", "model-wide behavior. `strict=True` rejects conversions for this integer field, including `\"4\"`,\n", "`4.0` and `True`; `extra=\"forbid\"` rejects undeclared keys. Pydantic can otherwise convert some\n", "compatible inputs, so choose this boundary deliberately rather than assuming every accepted\n", "input arrived in the expected type.\n", "\n", "Predict which rule refuses each payload. `ValidationError` reports a failed contract. Its\n", "`errors()` entries contain `loc`, the field location, and `type`, the failure category. Catching\n", "that expected exception lets the notebook inspect the failure and continue." ] }, { "cell_type": "code", "execution_count": null, "id": "bc4ca43942c7c8f4", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "intro_bad_requests = [\n", " {\"name\": \"mango\", \"quantity\": \"4\"},\n", " {\"name\": \"mango\", \"quantity\": True},\n", " {\"name\": \"\", \"quantity\": 4},\n", " {\"name\": \"mango\", \"quantity\": 0},\n", " {\"name\": \"mango\", \"quantity\": 4, \"approved\": True},\n", " {\"quantity\": 4},\n", "]\n", "for intro_bad_request in intro_bad_requests:\n", " try:\n", " IntroCourseRequest.model_validate(intro_bad_request)\n", " except ValidationError as intro_error:\n", " print([(item[\"loc\"], item[\"type\"]) for item in intro_error.errors(include_input=False)])\n", " else:\n", " raise AssertionError(\"An invalid input crossed the declared contract\")" ] }, { "cell_type": "markdown", "id": "e0a1461574b3910d", "metadata": {}, "source": [ "Use `model_dump()` for a Python dictionary, `model_dump_json()` for JSON text, and\n", "`model_validate_json()` to parse and validate JSON. `model_json_schema()` describes the contract;\n", "it is neither an instance's current values nor an invocation of the business handler." ] }, { "cell_type": "code", "execution_count": null, "id": "2fbae64da40afec4", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "intro_serialized = intro_request.model_dump_json()\n", "intro_schema = IntroCourseRequest.model_json_schema()\n", "assert IntroCourseRequest.model_validate_json(intro_serialized) == intro_request\n", "print(\"Actual values:\", intro_request.model_dump())\n", "print(\"Quantity contract:\", intro_schema[\"properties\"][\"quantity\"])\n", "assert intro_schema[\"properties\"][\"quantity\"][\"exclusiveMinimum\"] == 0" ] }, { "cell_type": "markdown", "id": "3111973aea1a20ec", "metadata": {}, "source": [ "Four is valid input to this schema even if the shop needs six. Pydantic checks the declared\n", "shape and constraints; the handler still needs authoritative stock, price and permission.\n", "Ordinary assignments to an existing instance are not automatically revalidated unless configured\n", "for assignment validation. This lesson validates new input at the boundary and uses the resulting\n", "values. Explain these limits before relying on a model object in a transaction or tool call.\n", "\n", "Chapter 2's full introduction expands this pattern with a separate data-repair checkpoint.\n", "This notebook contains the required pattern here so prior Pydantic experience is not needed.\n", "References: [models](https://docs.pydantic.dev/latest/concepts/models/),\n", "[fields](https://docs.pydantic.dev/latest/concepts/fields/), and\n", "[strict mode](https://docs.pydantic.dev/latest/concepts/strict_mode/)." ] }, { "cell_type": "markdown", "id": "2eef532fed9652e5", "metadata": {}, "source": [ "## A loop is a repeated decision with a stopping argument\n", "\n", "One tool call cannot answer every request. Lucy's agent may first inspect stock, then ask for\n", "prices, then prepare drafts, and finally explain what it found. An **agent loop** repeatedly\n", "obtains a model turn, executes admitted tool requests, appends their observations and decides\n", "whether another turn is allowed. It is ordinary control flow surrounding a model interface.\n", "\n", "A **transcript** is the ordered sequence of messages. A tool request carries a call identifier;\n", "the tool observation repeats it so a later reader can connect the result to the request. A\n", "**state machine** is a description of allowed states and transitions. Our loop starts running\n", "and eventually reaches a terminal reason such as completed, call limit or model failure.\n", "Terminal means this episode stops; it does not mean every requested business action succeeded.\n", "\n", "### Work one iteration by hand\n", "\n", "The following replay has two tool requests and a final answer. `iter` creates an iterator;\n", "`next` consumes its next item. A real provider could return different turns, but the program's\n", "responsibility to retain observations and enforce limits is unchanged. Predict the role sequence." ] }, { "cell_type": "code", "execution_count": null, "id": "2e8a6128657f06d6", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "intro_turns = [\n", " {\"call_id\": \"stock-1\", \"tool\": \"stock\"},\n", " {\"call_id\": \"price-1\", \"tool\": \"price\"},\n", " {\"answer\": \"A draft is ready for review.\"},\n", "]\n", "intro_transcript = []\n", "intro_tool_values = {\"stock\": 6, \"price\": 250}\n", "for intro_turn in intro_turns:\n", " intro_transcript.append({\"role\": \"assistant\", \"content\": intro_turn})\n", " if \"answer\" in intro_turn:\n", " break\n", " intro_transcript.append(\n", " {\n", " \"role\": \"tool\",\n", " \"tool_call_id\": intro_turn[\"call_id\"],\n", " \"value\": intro_tool_values[intro_turn[\"tool\"]],\n", " }\n", " )\n", "print([item[\"role\"] for item in intro_transcript])\n", "assert [item[\"role\"] for item in intro_transcript] == [\n", " \"assistant\",\n", " \"tool\",\n", " \"assistant\",\n", " \"tool\",\n", " \"assistant\",\n", "]" ] }, { "cell_type": "markdown", "id": "c8103d58c62daf7c", "metadata": {}, "source": [ "An assistant message requesting a tool is different from the tool observation. Do not replace\n", "the observation with an assistant's claim that a tool succeeded. The real exercise connects\n", "its admission function to the Chapter 2 dispatcher and retains actual handler results.\n", "\n", "### Bound the next attempt, not just the previous result\n", "\n", "Suppose each model attempt has a configured exposure of two pence and the budget is six.\n", "With four already charged, one more is admitted; with five already charged, it is refused.\n", "Equality at the boundary is allowed. Exposure is an estimate used for admission, not an invoice.\n", "A failed admitted attempt still consumes one call and its configured exposure. Otherwise a\n", "repeatedly failing provider appears free and can evade the bound." ] }, { "cell_type": "code", "execution_count": null, "id": "8422e325f5b70440", "metadata": { "tags": [ "foundation", "worked-example" ] }, "outputs": [], "source": [ "intro_spent = 0\n", "intro_attempts = 0\n", "intro_events = []\n", "while intro_attempts < 3 and intro_spent + 2 <= 6:\n", " intro_attempts += 1\n", " intro_spent += 2\n", " try:\n", " raise RuntimeError(\"authored provider failure\")\n", " except RuntimeError:\n", " intro_events.append((intro_attempts, intro_spent, \"failed\"))\n", "print(intro_events)\n", "assert intro_events == [(1, 2, \"failed\"), (2, 4, \"failed\"), (3, 6, \"failed\")]" ] }, { "cell_type": "markdown", "id": "85653e98aab09351", "metadata": {}, "source": [ "Move charging after the raised error in a copy of this example and predict the consequence\n", "before trying it with a fixed three-iteration outer bound. Keep that outer bound so the\n", "experiment cannot become an accidental infinite loop. This is the failure investigated in\n", "Unit B: accounting belongs at admission, before the provider attempt can fail.\n", "\n", "### Multiple limits and deterministic precedence\n", "\n", "A call-count limit bounds the number of provider attempts. A cost-exposure limit bounds their\n", "configured cumulative estimate. A tool-call limit bounds a different operation and must not\n", "be confused with model turns: one model turn may request multiple tools. If two refusal reasons\n", "apply, the implementation needs a declared precedence so the same input yields an explainable\n", "result. This lesson checks call count before cost exposure.\n", "\n", "The dataclass `ModelTurn` packages a content string and a tuple of calls. `ReplayModel.complete`\n", "returns the next authored turn. These are test doubles implementing the same small interface\n", "that the loop expects from a provider. They are not trained models. `Limits` supplies configured\n", "bounds. Your function decides admission; the surrounding loop records counters and invokes it.\n", "\n", "Before coding, draw a trace with columns for attempt number, exposure before, next cost,\n", "admission decision, provider outcome and exposure after. Include zero budget, exact fit and\n", "first-attempt failure. Then explain why a terminal status without counters is insufficient\n", "evidence for a bounded loop. The transfer changes costs and call identifiers, so memorizing\n", "the first transcript will not solve it." ] }, { "cell_type": "markdown", "id": "5a1ea5eddfa37fa0", "metadata": {}, "source": [ "## Choose an explicit starting point for this independent notebook\n", "\n", "This Unit B runs without Unit A. By default it prepares a **supplied reference starting point**\n", "and labels its provenance. It is not evidence that you built Unit A. To investigate your own\n", "successful implementation, set `LEARNER_HANDOFF` to its saved path before running the cell.\n", "An invalid selected file refuses; it is never silently replaced with the reference.\n", "\n", "`SourceTask` supplies copied-source execution and handoff validation; `RuntimeLab` supplies the\n", "controlled failure experiment. Their public operations are introduced beside the main exercise.\n", "The artifact stores identity and observations; no variables from another kernel are required.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "5f2cc44b9662347b", "metadata": { "tags": [ "setup", "handoff-selection" ] }, "outputs": [], "source": [ "LEARNER_HANDOFF = None" ] }, { "cell_type": "markdown", "id": "4ce6372c2e35de89", "metadata": {}, "source": [ "
Prepare and validate the supplied starting artifact\n" ] }, { "cell_type": "code", "execution_count": null, "id": "85318de650330a03", "metadata": { "tags": [ "setup", "independent-reference-start" ] }, "outputs": [], "source": [ "import json\n", "import runpy\n", "import shutil\n", "from pathlib import Path\n", "\n", "COURSE_INPUT = COURSE_WORK / \"ch03-unit-a-handoff-v1.json\"\n", "if LEARNER_HANDOFF is not None:\n", " learner_input = Path(LEARNER_HANDOFF).expanduser().resolve()\n", " if not learner_input.is_file():\n", " raise FileNotFoundError(\"The selected learner handoff does not exist\")\n", " if learner_input != COURSE_INPUT.resolve():\n", " shutil.copy2(learner_input, COURSE_INPUT)\n", " HANDOFF_ORIGIN = \"LEARNER_SELECTED\"\n", "else:\n", " reference_encoded = (\n", " \"c-qxj-E!Ny5x(bBptvF%nX#Sp%o)w_w2I9nnj=ddNjYi8)o^GLvQSebOMrIVjA!}~eZoFTcL9(DDLJt(\"\n", " \"qYIk^7Q2i6`LI~>U6GfPtn=c*;rjWXBG2mQT+~lxm9-?|K{%T--;pGi@j8u#V1k&rW=K;MvD~Kox}Lp+\"\n", " \"SI5BxnvH+QO2P9i;@KuAzXim7Gax5#J;#~PW=j%~v)QNlB8>3OfnUNls7iF5=eKUj(pd64<_#SJzoKM!\"\n", " \"Jg)?-LNWgOK5;!YBg+R(g1=n&>uthI7kPTkaLHsAvGO&O&V!>&4y=jnjYd?>j9f^*4wy{\"\n", " \"tULvs8nr3@eI2Lw@q>u+>~+fGGGpcI_4fGGFCM7pEb}})1MaY@W^hI8LAr7hEx!pIB!N!Q\"\n", " \"mD`RlKF%)b%5#d6XVRsbU_h<}aM;CHbW>$(2~MD^E1JbSMptB$mqbq!o{?6buLp(e0Y69inOx)vOT)6v\"\n", " \"!BkZy`HrcV+AA0ej`O!BE?kIyhffGoRLZqRBt8$GCXLIRY6q4OK#zFfC)KVHu3Fm64Tyl0j&9k5(9S%}\"\n", " \"@Ns|mac>0U4Y^~qwHEJ?t@^2IJmA!EIA=u~Kd4X|HjxnlvTzwiDl`TJ7YZvvlq@v}uH%p{uBK>1Yn|^P\"\n", " \"-Xt>&j8NFTN+XL^2V^WYGxk-wt(+Ed*w-~HBw5J37=z>(J=o(AbHVKVq8t7dxG-3|jVmEpLOr5zcA95s\"\n", " \"iP>d19Zx?*%TM#^qKU>Py_5$<;MQ2;@+6=_=Ih&|6UYaQih!n&ACVe_p~l<6w?}W_86UR{8+{Guj{c39\"\n", " \"J%6Lzq>MN6=&q5g-lM!sSlJa!#chRYu=xGc(I2DfcrqDb^8TxWEavh7rhe&0R4)>rqlS2y8jIz8^!Z&l\"\n", " \"pMU9T^7cthwBz>-NY7E8fN+v=N=|<{J|+dr){K03cj<#$Nl}#m\"\n", " \"M7(){h~u7!hUdOP2X;6-Dg5L+!vFGv{p|^F4c-<00-0~0ZPS&h)^5tU7ob_fp%`ET6(==mSAlJb*-N;R^Wrqji>QCzlN&xPOqz#bZcgN\"\n", " \"upO#@up>!LL&ml}uEr#CYgOPap3Fq=OS)\"\n", " \"0~@Gxcn%y8_ksOYy`dENplhH}KYTwI%n94r;7@@;(}QK@x9_BE&Ek*+j3LUhac=co~kC;kmBM#y`Iw\"\n", " \"l!2;3RW7z^tn8?_P}dyFFLnRK>mX53BIFO`?l%u^4@@?u%{TPdo_8cHO2Q{RGjcIvu*31AnoLT*M_Kstq9E2F~tS*<8YX\"\n", " \"YVK7nV<^+eV|dk{%Fi&`!SYjNKr$IbJm_RAoxtbgo;rkQFxb8Ua>@gZYARKq*z;x5#5tyDwK4x#1^?\"\n", " \"H=ct~HMGs-XBjijTH%!^$_Cqq`FylLZ#VCE$9z#wH!^89U^tz~}jRxeTWiW2%iGE)W$j1X*=8xSpMT^dOmyqKC*WWwq##f\"\n", " \"3573S(bUJLz~C80OJZIsv&II&Xe*K*3{{k9+3X^RM@R?8gHW\"\n", " )\n", " reference_code = zlib.decompress(base64.b85decode(reference_encoded)).decode()\n", " reference_namespace = {\"COURSE_ROOT\": COURSE_ROOT, \"COURSE_WORK\": COURSE_WORK}\n", " exec(compile(reference_code, \"\", \"exec\"), reference_namespace)\n", " if not COURSE_INPUT.is_file():\n", " raise RuntimeError(\"The supplied reference did not produce its handoff\")\n", " HANDOFF_ORIGIN = \"SUPPLIED_REFERENCE\"\n", "print(\"Starting evidence:\", HANDOFF_ORIGIN)\n", "print(\"The core task below validates the selected artifact before using it.\")" ] }, { "cell_type": "markdown", "id": "b8408261a3789c7e", "metadata": {}, "source": [ "
\n" ] }, { "cell_type": "markdown", "id": "b435c7ed65391731", "metadata": {}, "source": [ "## Understand the supplied execution interface\n", "\n", "The course runtime is provided so your implementation can be connected to real callers and\n", "storage. `SourceTask(ROOT, chapter)` makes a private copy. `install(source)` replaces only the\n", "declared function; `visible()` invokes the real chapter probe; `save(path, result)` retains a\n", "successful implementation and its evidence. `load(path)` checks the saved identities and hashes.\n", "`inject_failure()` changes the declared boundary; `repair(fragment)` replaces that broken fragment.\n", "`close()` removes the scratch copy after you retain evidence. These methods are supplied harness\n", "operations, not additional packages you must discover or install.\n", "\n", "`RuntimeLab` provides the same copied-source failure experiment without the complete-function\n", "construction layer. Its `run` method records exit status, observations and the compared expectation.\n", "A subprocess log from an unfinished learner implementation is feedback about that implementation;\n", "it is not a successful connection. A syntax error in the notebook cell itself is a separate issue\n", "to fix. The task below names which interface it uses.\n", "\n", "For direct-function units, the visible driver calls your callback without installing a source\n", "string. In either case, trace where your code is invoked. Supplied fixtures, database wrappers and\n", "replay models are labelled infrastructure; your own implementation and changed-case explanation\n", "are the evidence of learning.\n" ] }, { "cell_type": "markdown", "id": "db9a7f7bcec5022b", "metadata": {}, "source": [ "## Main practical: construct, connect and challenge\n", "\n", "\n", "\n", "The bounded loop works when the provider cooperates. Now a provider fails on its first admitted call. You will break a temporary copy of the actual Chapter 3 learner implementation, repair the accounting line, and test a second failure shape.\n", "\n", "This unit runs reviewed local course code in bounded subprocesses. It is not a security sandbox.\n", "\n", "## 1. Verify the Unit A handoff and load the real experiment" ] }, { "cell_type": "code", "execution_count": null, "id": "08f310c7cd12e3f4", "metadata": { "tags": [ "setup", "handoff-consumer" ] }, "outputs": [], "source": [ "import json\n", "import os\n", "import sys\n", "from pathlib import Path\n", "\n", "assert sys.version_info >= (3, 14)\n", "\n", "ROOT = COURSE_ROOT\n", "\n", "\n", "def run_book(relative):\n", " previous = Path.cwd()\n", " try:\n", " os.chdir(ROOT)\n", " return runpy.run_path(str(ROOT / relative))\n", " finally:\n", " os.chdir(previous)\n", "\n", "\n", "HANDOFF_PATH = Path(\"ch03-unit-a-handoff-v1.json\")\n", "handoff_status = \"MISSING\"\n", "if HANDOFF_PATH.is_file():\n", " handoff = json.loads(HANDOFF_PATH.read_text(encoding=\"utf-8\"))\n", " handoff_status = \"VERIFIED\" if handoff.get(\"status\") == \"COMPLETED\" else \"INVALID\"\n", "print(\"UNIT_A_HANDOFF\", handoff_status)\n", "\n", "support = runpy.run_path(str(ROOT / \"book/always_on/educator/runtime_labs_v1.py\"))\n", "RuntimeLab = support[\"RuntimeLab\"]" ] }, { "cell_type": "markdown", "id": "d34da6dd97edf2a1", "metadata": {}, "source": [ "Predict the baseline observation. If the provider fails after admission, should the local result record zero or one model attempt? Should the configured five-pence exposure disappear?\n", "\n", "## 2. Break the real source copy" ] }, { "cell_type": "code", "execution_count": null, "id": "6a73b000c11d8b8d", "metadata": { "tags": [ "failure-experiment" ] }, "outputs": [], "source": [ "lab = RuntimeLab(ROOT, 3)\n", "baseline = lab.run(\"BASELINE\", expected=lab.spec[\"expected_baseline\"])\n", "lab.break_source()\n", "broken = lab.run(\"BROKEN\", expected=lab.spec[\"expected_broken\"])\n", "print(json.dumps({\"baseline\": baseline[\"observation\"], \"broken\": broken[\"observation\"]}, indent=2))\n", "assert baseline[\"status\"] == \"PASS\"\n", "assert broken[\"status\"] == \"PASS\"" ] }, { "cell_type": "markdown", "id": "6f043f674b089a2c", "metadata": {}, "source": [ "The broken source changes `exposure += limits.estimated_call_pence` to `exposure += 0`. The model-call counter still advances. This separates attempted calls from configured cost exposure and makes repeated failures look free.\n", "\n", "## 3. Construct the repair\n", "\n", "Return the complete replacement for the one marked broken fragment. The starter repeats the broken code and therefore fails the baseline contract." ] }, { "cell_type": "code", "execution_count": null, "id": "b7fb464bb7091dfe", "metadata": { "tags": [ "exercise", "learner-owned", "ch03-repair" ] }, "outputs": [], "source": [ "def repair_fragment():\n", " return \"exposure += 0\"" ] }, { "cell_type": "markdown", "id": "99b6b060ae2909e3", "metadata": {}, "source": [ "
Hint 1 — the decision\n", "\n", "Repair the accounting statement; do not change the provider fixture, expected result, or call counter.\n", "\n", "
\n", "\n", "
Hint 2 — the evidence\n", "\n", "Compare `baseline[\"observation\"]` with `broken[\"observation\"]`. Only `estimated_pence` should change.\n", "\n", "
\n", "\n", "
Hint 3 — the structure\n", "\n", "The replacement adds `limits.estimated_call_pence` to `exposure` before `model.complete` begins.\n", "\n", "
" ] }, { "cell_type": "code", "execution_count": null, "id": "713da0bde80d6b28", "metadata": { "tags": [ "integration", "learner-path" ] }, "outputs": [], "source": [ "lab.repair(repair_fragment())\n", "student_repair = lab.run(\"STUDENT_REPAIR\", expected=lab.spec[\"expected_baseline\"])\n", "REPAIR_PASSED = student_repair[\"status\"] == \"PASS\"\n", "print(json.dumps(student_repair[\"observation\"], indent=2))\n", "print(\"REAL_SOURCE_REPAIR\", \"PASSED\" if REPAIR_PASSED else \"NEEDS_WORK\")\n", "lab.close()" ] }, { "cell_type": "markdown", "id": "1be7812ac64c7911", "metadata": {}, "source": [ "The original checkout is never edited. `RuntimeLab` fingerprints it, copies the real source and probe, executes the copy, and checks the original again after every run.\n", "\n", "## 4. Challenge two termination paths\n", "\n", "Use the real Chapter 3 loop. First, repeat a tool-call identifier. Then fail on the first provider call. Predict the result status and counters before running." ] }, { "cell_type": "code", "execution_count": null, "id": "854a0f85cca6917f", "metadata": { "tags": [ "challenge" ] }, "outputs": [], "source": [ "chapter3 = run_book(\"book/always_on/learner/ch03.py\")\n", "ToolCall = chapter3[\"ToolCall\"]\n", "ModelTurn = chapter3[\"ModelTurn\"]\n", "ReplayModel = chapter3[\"ReplayModel\"]\n", "Limits = chapter3[\"Limits\"]\n", "run_loop = chapter3[\"run_loop\"]\n", "dispatcher = chapter3[\"shop_tools\"][\"build_tools\"](chapter3[\"shop_tools\"][\"SHOP\"])\n", "\n", "repeated = run_loop(\n", " ReplayModel(\n", " [\n", " ModelTurn(calls=(ToolCall(id=\"same\", name=\"list_stock\", arguments={}),)),\n", " ModelTurn(calls=(ToolCall(id=\"same\", name=\"list_stock\", arguments={}),)),\n", " ]\n", " ),\n", " dispatcher,\n", " chapter3[\"messages\"],\n", ")\n", "\n", "\n", "class FailedModel:\n", " def complete(self, *args, **kwargs):\n", " raise chapter3[\"ModelError\"](\"fixture failure\")\n", "\n", "\n", "failed = run_loop(\n", " FailedModel(),\n", " dispatcher,\n", " chapter3[\"messages\"],\n", " limits=Limits(estimated_call_pence=5, model_budget_pence=10),\n", ")\n", "print(\"repeated\", repeated.status, repeated.model_calls, repeated.tool_calls)\n", "print(\"failed\", failed.status, failed.model_calls, failed.estimated_cost_pence)\n", "assert repeated.status == \"REPEATED_CALL_ID\"\n", "assert (failed.status, failed.model_calls, failed.estimated_cost_pence) == (\"MODEL_FAILED\", 1, 5)" ] }, { "cell_type": "markdown", "id": "46af52e9456d99ea", "metadata": {}, "source": [ "## 5. Transfer the repair\n", "\n", "Change the copied probe so the failed model is invoked twice through two separate `run_loop` calls and retain both observations. Explain why each run begins a new local budget and why a durable organization later needs budget reservations outside this in-memory loop.\n", "\n", "The instructor holdout also applies your fragment to a fresh copy and uses an unseen configured estimate. A repair that prints five, changes expected data, or special-cases the visible observation will fail.\n", "\n", "## Exit ticket\n", "\n", "Submit the baseline, broken and repaired observations; your fragment; the repeated-ID and provider-failure results; and a causal explanation naming what would falsify it." ] }, { "cell_type": "code", "execution_count": null, "id": "497949b878815bea", "metadata": { "tags": [ "exercise-report" ] }, "outputs": [], "source": [ "exercise_report = {\n", " \"unit\": \"ch03-b\",\n", " \"attempted\": 1,\n", " \"completed\": int(REPAIR_PASSED),\n", " \"failed\": int(not REPAIR_PASSED),\n", " \"skipped\": 0,\n", " \"connection\": \"PASSED\" if REPAIR_PASSED else \"NOT_READY\",\n", " \"handoff\": handoff_status,\n", "}\n", "print(\"EXERCISE_REPORT=\" + json.dumps(exercise_report, sort_keys=True))" ] }, { "cell_type": "markdown", "id": "8a94c8c0695f7eaa", "metadata": {}, "source": [ "## Changed-constraint construction: Admit a variable-cost next model attempt\n", "\n", "**Allow twenty minutes.** Spend three minutes predicting, ten implementing and tracing, five\n", "on a new case of your own, and two explaining the surviving limitation. This is dedicated work,\n", "not an invitation to run a supplied answer. Both units revisit the same invariant after different\n", "core experiences; in Unit B, attempt this task from memory before consulting Unit A.\n", "\n", "Implement transfer_check(state). Fields calls, max_calls, spent, next_cost and budget are validated nonnegative integers. Return CALL_LIMIT first when calls >= max_calls, otherwise COST_LIMIT when spent + next_cost > budget, otherwise CALL. Equality at the money boundary is allowed. The next-cost estimate can differ on every attempt.\n", "\n", "Write your expected values before running the table. Keep one accepted case and one refusal.\n", "Your function is passed directly into the driver below. The driver copies inputs and checks\n", "they remain unchanged; it does not replace your implementation with the reference answer.\n", "\n", "
Hint 1 — identify the authoritative inputs\n", "Name the source field for each output value. Which input changes while the rule remains the same?\n", "
\n", "
Hint 2 — choose the boundary cases\n", "Start with exact empty, exact equality and one value on each side of the boundary where valid.\n", "Do not add a special case for a visible product name or operation identity.\n", "
\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a35619d02ea181f5", "metadata": { "tags": [ "exercise", "transfer-owned" ] }, "outputs": [], "source": [ "def transfer_check(state):\n", " raise NotImplementedError(\"Decide before admitting the next attempt\")" ] }, { "cell_type": "code", "execution_count": null, "id": "ad8b0892aa7a84ca", "metadata": { "tags": [ "assessment", "transfer-invocation" ] }, "outputs": [], "source": [ "import copy\n", "import json\n", "\n", "TRANSFER_CASES = [\n", " (\"exact fit\", [{\"calls\": 1, \"max_calls\": 3, \"spent\": 4, \"next_cost\": 3, \"budget\": 7}], \"CALL\"),\n", " (\n", " \"next attempt too costly\",\n", " [{\"calls\": 1, \"max_calls\": 3, \"spent\": 4, \"next_cost\": 4, \"budget\": 7}],\n", " \"COST_LIMIT\",\n", " ),\n", " (\n", " \"two exhausted limits\",\n", " [{\"calls\": 3, \"max_calls\": 3, \"spent\": 7, \"next_cost\": 1, \"budget\": 7}],\n", " \"CALL_LIMIT\",\n", " ),\n", " (\n", " \"zero allowed attempts\",\n", " [{\"calls\": 0, \"max_calls\": 0, \"spent\": 0, \"next_cost\": 0, \"budget\": 0}],\n", " \"CALL_LIMIT\",\n", " ),\n", "]\n", "\n", "\n", "def same_transfer_value(actual, expected):\n", " if type(actual) is not type(expected):\n", " return False\n", " if isinstance(expected, dict):\n", " return actual.keys() == expected.keys() and all(\n", " same_transfer_value(actual[key], value) for key, value in expected.items()\n", " )\n", " if isinstance(expected, list):\n", " return len(actual) == len(expected) and all(\n", " same_transfer_value(a, e) for a, e in zip(actual, expected, strict=True)\n", " )\n", " return actual == expected\n", "\n", "\n", "def run_transfer(candidate, cases):\n", " observations = []\n", " for label, arguments, expected in cases:\n", " supplied = copy.deepcopy(arguments)\n", " before = copy.deepcopy(supplied)\n", " raised = None\n", " try:\n", " actual = candidate(*supplied)\n", " except NotImplementedError:\n", " raised = \"NotImplementedError\"\n", " actual = {\"unfinished\": True}\n", " except Exception as error:\n", " raised = type(error).__name__\n", " actual = {\"raises\": raised}\n", " expects_error = isinstance(expected, dict) and set(expected) == {\"raises\"}\n", " correct = (\n", " raised == expected[\"raises\"]\n", " if expects_error\n", " else (raised is None and same_transfer_value(actual, expected))\n", " )\n", " passed = correct and same_transfer_value(supplied, before)\n", " observations.append(\n", " {\"case\": label, \"expected\": expected, \"observed\": actual, \"passed\": passed}\n", " )\n", " print(\"PASS\" if passed else \"NEEDS_WORK\", label, \"expected\", expected, \"observed\", actual)\n", " return observations\n", "\n", "\n", "transfer_observations = run_transfer(transfer_check, TRANSFER_CASES)\n", "TRANSFER_PASSED = all(row[\"passed\"] for row in transfer_observations)\n", "print(\"TRANSFER_STATUS\", \"PASS\" if TRANSFER_PASSED else \"NEEDS_WORK\")" ] }, { "cell_type": "markdown", "id": "60d40cc812b7353e", "metadata": {}, "source": [ "### Design a counterexample and retrieve the mechanism\n", "\n", "Add one new case with an independently calculated expected outcome to `TRANSFER_CASES` and rerun\n", "the driver. Change one condition at a time. Then deliberately replace your candidate with a\n", "constant answer in a temporary copy and show a case that rejects it. Restore your implementation.\n", "Explain why that counterexample is stronger than repeating the original example with a new name.\n", "\n", "Without viewing the worked example, write the invariant in words and trace one observed value\n", "back to its input. Identify which part is a local fixture result and which claim would need a\n", "live provider, host or external-system observation. Keep a first attempt even if you used a hint.\n" ] }, { "cell_type": "markdown", "id": "83e7820163301b29", "metadata": {}, "source": [ "## Save your evidence and explain the result\n", "\n", "Fill the prediction notes and your explanation before saving. Include the exact observed value,\n", "the input or retained row that caused it, your code's invocation point, one failed hypothesis,\n", "and the strongest claim the evidence still cannot support. A completed code cell alone does not\n", "earn explanation credit. Do not label reference-start behavior as your own Unit A construction.\n", "\n", "Keep this edited notebook, the Markdown if used for notes, saved handoff files, and the JSON record\n", "below. Your work folder survives scratch cleanup and can be reopened in a new kernel. An instructor\n", "can ask for an unseen case after the visible checks; keep your implementation general.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0360a0cc249b5f9b", "metadata": { "tags": [ "course-report", "retained-evidence" ] }, "outputs": [], "source": [ "explanation_notes = {\n", " \"causal_trace\": \"Explain the input, learner invocation and observed result.\",\n", " \"failed_hypothesis\": \"Describe a prediction the evidence changed.\",\n", " \"remaining_limit\": \"Name the guarantee not established by this experiment.\",\n", "}\n", "course_submission = {\n", " \"unit\": \"ch03-b\",\n", " \"planned_minutes\": 90,\n", " \"starting_evidence\": globals().get(\"HANDOFF_ORIGIN\", \"INDEPENDENT_UNIT_A\"),\n", " \"prediction\": prediction_notes,\n", " \"explanation\": explanation_notes,\n", " \"core_report\": exercise_report,\n", " \"transfer\": transfer_observations,\n", " \"explanation_review\": \"HUMAN_REVIEW_REQUIRED\",\n", "}\n", "submission_path = COURSE_WORK / \"ch03-b-submission-v1.json\"\n", "submission_path.write_text(\n", " json.dumps(course_submission, indent=2, sort_keys=True), encoding=\"utf-8\"\n", ")\n", "print(\"Saved evidence:\", submission_path)\n", "print(\n", " \"COURSE_REPORT=\"\n", " + json.dumps(\n", " {\n", " \"unit\": \"ch03-b\",\n", " \"transfer_passed\": TRANSFER_PASSED,\n", " \"starting_evidence\": course_submission[\"starting_evidence\"],\n", " \"edition\": \"student\",\n", " },\n", " sort_keys=True,\n", " )\n", ")" ] }, { "cell_type": "markdown", "id": "603ba4dc49b4", "metadata": { "tags": [ "profrod-community" ] }, "source": [ "## Keep building with Prof Rod\n", "\n", "Found this material through a colleague, classroom or shared download? [Get the complete book at profrod.ai/book](https://profrod.ai/book) and [join the Prof Rod learner community](https://profrod.ai/community). Bring one result, one question or one failure you learned from. Share this resource with another learner and keep its source links with it so they can find the full course and future updates.\n" ] } ], "metadata": { "authors": [ { "name": "Prof Rod", "website": "https://profrod.ai" } ], "course": { "book_url": "https://profrod.ai/book", "community_url": "https://profrod.ai/community", "distribution_version": "2026-09-10", "edition": "nineteen-chapter-v1", "instructor": false, "lesson_id": "agent-loop", "planned_minutes": 90, "resource_id": "profrod-sovereign-agent-ch03-b-failed-call-accounting-exercise", "self_contained_runtime": true, "source_basis": "444c5f6", "source_unit": "ch03-b", "source_url": "https://github.com/profrodai/sovereign-agent", "unit": "ch03-b" }, "jupytext": { "notebook_metadata_filter": "all", "text_representation": { "extension": ".md", "format_name": "markdown", "format_version": "1.3", "jupytext_version": "1.19.5" } }, "kernelspec": { "display_name": "Python 3.14", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.14" } }, "nbformat": 4, "nbformat_minor": 5 }