Bounded Agent Loop: Unit A Exercises

Student edition · 90 minutes of dedicated work · 2026-09-09

This is one of two practical units for Chapter 3. Unit A constructs and connects the mechanism; Unit B investigates a controlled failure, repairs it and transfers the invariant. Each is a complete ninety-minute session, with its own setup and required conceptual introductions. Basic Python variables, conditions, loops, functions, lists and dictionaries are the starting knowledge. Libraries and specialized concepts used here are introduced below before the main task.

By the end you should be able to:

1. Explain the chapter's mechanism using a prediction and an observed intermediate result.
2. Implement next-call admission under call-count and cost-exposure limits; trace actual tool observations through the bounded loop.
3. Solve admit a variable-cost next model attempt using changed inputs and an independent expectation.
4. Retain your implementation, failed/corrected observations, causal explanation and limits.

Installation is preparation time. These are planning estimates, not measured completion times. Use the reference primers when a term is unfamiliar; in Unit B retrieve an explanation before re-reading it. Run All checks that the artifact executes. Unfinished student functions deliberately produce NEEDS_WORK. Keep your first attempt before opening answers.

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.

Run the self-contained setup

Use a Python 3.14 Jupyter kernel and Pydantic 2. If needed, run %pip install "pydantic==2.13.4" once in a separate cell and restart the kernel. Package installation needs internet; the lesson itself needs no repository download, API key or prior notebook. The complete source runtime uses Python 3.14, so this edition does not claim compatibility with a hosted notebook service's default interpreter.

The collapsed cell contains 89 frozen teaching files. Base85 represents compressed bytes as text; zlib decompresses them; SHA-256 checks that the decoded files match this edition. These are supplied packaging operations, not learner algorithms. tempfile creates an isolated working copy; Path handles file locations; sys.path tells Python where the supplied modules live. The code is available for inspection below and performs no package installation itself. The subsequent lesson teaches the libraries used by the mechanisms you will implement.

Run setup on every fresh kernel. It writes scratch runtime files separately from your retained practical-work/ch03-a folder. Rerunning setup restores the frozen support files and keeps your saved work. Restarting a kernel clears variables, not saved submission files. Source basis: Sovereign Agent 444c5f6. Some tasks use reviewed local subprocesses; they are not an OS sandbox.

Supplied offline setup and teaching files

The complete supplied setup cell is in the downloadable notebook. Run it there before attempting the cells below. This web edition omits only that compressed setup payload.

Commit to a prediction before the examples

prediction_notes = {
    "prediction": "Write the expected behavior before running the worked example.",
    "reason": "Name the input and rule behind that prediction.",
    "falsifier": "Name an observation that would prove the explanation wrong.",
    "revision": "After execution, explain what changed in your understanding.",
}

Reading the Python vocabulary used in this notebook

You need basic assignments, if, loops, functions, lists and dictionaries. The less familiar features used by the supplied code are introduced here. A library is reusable code that Python can import. The standard library ships with Python; Pydantic is an additional package. An import makes a name available, but does not mean that you have completed the exercise.

JSON is text for exchanging structured values. A Python dictionary is an in-memory object; the JSON representation is a string. Use json.dumps to encode and json.loads to decode. Decoding proves that text has valid JSON syntax, not that its fields match our business contract. Predict which of the following two decoded objects could describe a stock count.

import json

intro_data = {"sku": "MANGO", "count": 3}
intro_text = json.dumps(intro_data, sort_keys=True)
print(type(intro_data).__name__, type(intro_text).__name__, intro_text)
print(json.loads(intro_text))
print("Also valid JSON:", json.loads('["not", "a", "stock", "record"]'))
assert json.loads(intro_text) == intro_data

The first result is a dictionary; the second is a list. Before indexing a decoded object, check the shape that your function promises to accept. An exception interrupts the normal path. raise ValueError(...) refuses an invalid value; try/except lets a caller inspect that expected refusal. Catch the expected class, rather than turning every programming error into apparent success. finally runs cleanup even when an earlier operation raises.

An annotation, such as count: int, documents the expected type. It does not by itself enforce the type at runtime. A class defines a kind of object; an instance holds one object's data. @dataclass asks Python to generate routine construction and comparison methods from annotated fields. frozen=True prevents ordinary reassignment of the instance's fields; it does not make every object nested inside those fields immutable. A method is a function attached to a class; self refers to the instance receiving the call.

from dataclasses import dataclass


@dataclass(frozen=True)
class IntroObservation:
    operation: str
    count: int


intro_observation = IntroObservation("count-mango", 3)
print(intro_observation.operation, intro_observation.count)
assert intro_observation == IntroObservation("count-mango", 3)

A callback is a function passed to another function. This is how the classroom harness invokes your implementation. The argument candidate below is a function object; parentheses perform the call. Predict the two answers before execution, then trace the result to the callback.

def intro_apply(candidate, value):
    return {"input": value, "observed": candidate(value)}


def intro_double(value):
    return value * 2


print(intro_apply(intro_double, 3))
print(intro_apply(lambda value: value + 2, 3))
assert intro_apply(intro_double, 3)["observed"] == 6

lambda value: value + 2 is a small anonymous function. A closure is a function that retains access to values from its surrounding scope. It can bind a tool to a shop snapshot. A shallow copy duplicates only the outer container; copy.deepcopy also copies nested containers used in these fixtures. A set stores distinct values; required <= allowed asks whether every required item is allowed. frozenset is the corresponding immutable set. A tuple groups ordered values; (value,) is a one-item tuple, including the comma.

Paths and cleanup. Path represents a filesystem location. path / "file.json" constructs a child path; read_text and write_text read and write text. A context manager, used with with, manages entry and exit. A temporary-directory context removes its contents on exit. Save your submission outside temporary runtime directories. Reopening a file is different from reusing a Python variable: the former tests retained bytes, while the latter only tests this kernel.

from pathlib import Path
from tempfile import TemporaryDirectory

with TemporaryDirectory() as intro_folder:
    intro_path = Path(intro_folder) / "observation.json"
    intro_path.write_text(json.dumps(intro_data), encoding="utf-8")
    intro_reopened = json.loads(intro_path.read_text(encoding="utf-8"))
    assert intro_reopened == intro_data
    print("Read from a file:", intro_reopened)

Retrieval check: explain JSON versus a dictionary, annotation versus validation, class versus instance, and defining a callback versus invoking it. Change the callback above so an incorrect implementation visibly changes the observed output. This distinction will matter when grading your connected work. Reference: Python's JSON, dataclasses, and pathlib documentation.

Pydantic: turn an input dictionary into a checked object

Pydantic is an additional Python library for validating data. Its model is a class describing fields, not a neural network. Inherit from BaseModel, declare annotated fields, then call model_validate on incoming data. A field without a default is required. A field with a default can be omitted. The result is an instance whose values you read with dot notation.

from pydantic import BaseModel, ConfigDict, Field, ValidationError


class IntroCourseRequest(BaseModel):
    model_config = ConfigDict(strict=True, extra="forbid")
    name: str = Field(min_length=1)
    quantity: int = Field(gt=0, le=1000)
    note: str = ""


intro_request = IntroCourseRequest.model_validate({"name": "mango", "quantity": 4})
print(intro_request.name, intro_request.quantity, repr(intro_request.note))
assert intro_request.note == ""

The annotation says the field's type. Field supplies constraints: gt=0 means greater than zero, le=1000 means at most 1000, and min_length=1 excludes an empty name. ConfigDict sets model-wide behavior. strict=True rejects conversions for this integer field, including "4", 4.0 and True; extra="forbid" rejects undeclared keys. Pydantic can otherwise convert some compatible inputs, so choose this boundary deliberately rather than assuming every accepted input arrived in the expected type.

Predict which rule refuses each payload. ValidationError reports a failed contract. Its errors() entries contain loc, the field location, and type, the failure category. Catching that expected exception lets the notebook inspect the failure and continue.

intro_bad_requests = [
    {"name": "mango", "quantity": "4"},
    {"name": "mango", "quantity": True},
    {"name": "", "quantity": 4},
    {"name": "mango", "quantity": 0},
    {"name": "mango", "quantity": 4, "approved": True},
    {"quantity": 4},
]
for intro_bad_request in intro_bad_requests:
    try:
        IntroCourseRequest.model_validate(intro_bad_request)
    except ValidationError as intro_error:
        print([(item["loc"], item["type"]) for item in intro_error.errors(include_input=False)])
    else:
        raise AssertionError("An invalid input crossed the declared contract")

Use model_dump() for a Python dictionary, model_dump_json() for JSON text, and model_validate_json() to parse and validate JSON. model_json_schema() describes the contract; it is neither an instance's current values nor an invocation of the business handler.

intro_serialized = intro_request.model_dump_json()
intro_schema = IntroCourseRequest.model_json_schema()
assert IntroCourseRequest.model_validate_json(intro_serialized) == intro_request
print("Actual values:", intro_request.model_dump())
print("Quantity contract:", intro_schema["properties"]["quantity"])
assert intro_schema["properties"]["quantity"]["exclusiveMinimum"] == 0

Four is valid input to this schema even if the shop needs six. Pydantic checks the declared shape and constraints; the handler still needs authoritative stock, price and permission. Ordinary assignments to an existing instance are not automatically revalidated unless configured for assignment validation. This lesson validates new input at the boundary and uses the resulting values. Explain these limits before relying on a model object in a transaction or tool call.

Chapter 2's full introduction expands this pattern with a separate data-repair checkpoint. This notebook contains the required pattern here so prior Pydantic experience is not needed. References: models, fields, and strict mode.

A loop is a repeated decision with a stopping argument

One tool call cannot answer every request. Lucy's agent may first inspect stock, then ask for prices, then prepare drafts, and finally explain what it found. An agent loop repeatedly obtains a model turn, executes admitted tool requests, appends their observations and decides whether another turn is allowed. It is ordinary control flow surrounding a model interface.

A transcript is the ordered sequence of messages. A tool request carries a call identifier; the tool observation repeats it so a later reader can connect the result to the request. A state machine is a description of allowed states and transitions. Our loop starts running and eventually reaches a terminal reason such as completed, call limit or model failure. Terminal means this episode stops; it does not mean every requested business action succeeded.

Work one iteration by hand

The following replay has two tool requests and a final answer. iter creates an iterator; next consumes its next item. A real provider could return different turns, but the program's responsibility to retain observations and enforce limits is unchanged. Predict the role sequence.

intro_turns = [
    {"call_id": "stock-1", "tool": "stock"},
    {"call_id": "price-1", "tool": "price"},
    {"answer": "A draft is ready for review."},
]
intro_transcript = []
intro_tool_values = {"stock": 6, "price": 250}
for intro_turn in intro_turns:
    intro_transcript.append({"role": "assistant", "content": intro_turn})
    if "answer" in intro_turn:
        break
    intro_transcript.append(
        {
            "role": "tool",
            "tool_call_id": intro_turn["call_id"],
            "value": intro_tool_values[intro_turn["tool"]],
        }
    )
print([item["role"] for item in intro_transcript])
assert [item["role"] for item in intro_transcript] == [
    "assistant",
    "tool",
    "assistant",
    "tool",
    "assistant",
]

An assistant message requesting a tool is different from the tool observation. Do not replace the observation with an assistant's claim that a tool succeeded. The real exercise connects its admission function to the Chapter 2 dispatcher and retains actual handler results.

Bound the next attempt, not just the previous result

Suppose each model attempt has a configured exposure of two pence and the budget is six. With four already charged, one more is admitted; with five already charged, it is refused. Equality at the boundary is allowed. Exposure is an estimate used for admission, not an invoice. A failed admitted attempt still consumes one call and its configured exposure. Otherwise a repeatedly failing provider appears free and can evade the bound.

intro_spent = 0
intro_attempts = 0
intro_events = []
while intro_attempts < 3 and intro_spent + 2 <= 6:
    intro_attempts += 1
    intro_spent += 2
    try:
        raise RuntimeError("authored provider failure")
    except RuntimeError:
        intro_events.append((intro_attempts, intro_spent, "failed"))
print(intro_events)
assert intro_events == [(1, 2, "failed"), (2, 4, "failed"), (3, 6, "failed")]

Move charging after the raised error in a copy of this example and predict the consequence before trying it with a fixed three-iteration outer bound. Keep that outer bound so the experiment cannot become an accidental infinite loop. This is the failure investigated in Unit B: accounting belongs at admission, before the provider attempt can fail.

Multiple limits and deterministic precedence

A call-count limit bounds the number of provider attempts. A cost-exposure limit bounds their configured cumulative estimate. A tool-call limit bounds a different operation and must not be confused with model turns: one model turn may request multiple tools. If two refusal reasons apply, the implementation needs a declared precedence so the same input yields an explainable result. This lesson checks call count before cost exposure.

The dataclass ModelTurn packages a content string and a tuple of calls. ReplayModel.complete returns the next authored turn. These are test doubles implementing the same small interface that the loop expects from a provider. They are not trained models. Limits supplies configured bounds. Your function decides admission; the surrounding loop records counters and invokes it.

Before coding, draw a trace with columns for attempt number, exposure before, next cost, admission decision, provider outcome and exposure after. Include zero budget, exact fit and first-attempt failure. Then explain why a terminal status without counters is insufficient evidence for a bounded loop. The transfer changes costs and call identifiers, so memorizing the first transcript will not solve it.

Understand the supplied execution interface

The course runtime is provided so your implementation can be connected to real callers and storage. SourceTask(ROOT, chapter) makes a private copy. install(source) replaces only the declared function; visible() invokes the real chapter probe; save(path, result) retains a successful implementation and its evidence. load(path) checks the saved identities and hashes. inject_failure() changes the declared boundary; repair(fragment) replaces that broken fragment. close() removes the scratch copy after you retain evidence. These methods are supplied harness operations, not additional packages you must discover or install.

RuntimeLab provides the same copied-source failure experiment without the complete-function construction layer. Its run method records exit status, observations and the compared expectation. A subprocess log from an unfinished learner implementation is feedback about that implementation; it is not a successful connection. A syntax error in the notebook cell itself is a separate issue to fix. The task below names which interface it uses.

For direct-function units, the visible driver calls your callback without installing a source string. In either case, trace where your code is invoked. Supplied fixtures, database wrappers and replay models are labelled infrastructure; your own implementation and changed-case explanation are the evidence of learning.

Main practical: construct, connect and challenge

Lucy asks what needs ordering. One model turn requests stock, another requests two drafts, and a final turn explains the results. You will implement the admission decision that governs every model call, connect it to a real Chapter 2 tool dispatcher, and retain the transcript for Unit B.

The setup above supplies the required source; use Python 3.14. It uses authored model turns and makes no network call or purchase.

1. Locate the cumulative code

import copy
import json
import os
import runpy
import sys
from dataclasses import dataclass
from pathlib import Path

assert sys.version_info >= (3, 14)

ROOT = COURSE_ROOT


def run_book(relative):
    previous = Path.cwd()
    try:
        os.chdir(ROOT)
        return runpy.run_path(str(ROOT / relative))
    finally:
        os.chdir(previous)


chapter2 = run_book("book/always_on/learner/ch02.py")
ToolCall = chapter2["ToolCall"]
dispatcher = chapter2["build_tools"](chapter2["SHOP"])
print("tools", [schema["function"]["name"] for schema in dispatcher.schemas()])

Predict the transcript roles for stock lookup, two draft calls and a final answer. A tool observation must retain the request's call identifier.

2. Represent authored model turns

class ModelError(RuntimeError):
    pass


@dataclass(frozen=True)
class ModelTurn:
    content: str = ""
    calls: tuple = ()

    def message(self):
        message = {"role": "assistant", "content": self.content}
        if self.calls:
            message["tool_calls"] = [
                {
                    "id": call.id,
                    "type": "function",
                    "function": {
                        "name": call.name,
                        "arguments": json.dumps(call.arguments, sort_keys=True),
                    },
                }
                for call in self.calls
            ]
        return message


class ReplayModel:
    def __init__(self, turns):
        self.turns = iter(turns)

    def complete(self, messages, tools):
        try:
            return next(self.turns)
        except StopIteration:
            raise ModelError("fixture exhausted") from None


OPENING_TURNS = [
    ModelTurn(calls=(ToolCall(id="stock-1", name="list_stock", arguments={}),)),
    ModelTurn(
        calls=(
            ToolCall(
                id="draft-v", name="draft_order", arguments={"sku": "SKU-VANILLA", "quantity": 6}
            ),
            ToolCall(
                id="draft-s", name="draft_order", arguments={"sku": "SKU-STRAWBERRY", "quantity": 4}
            ),
        )
    ),
    ModelTurn("Drafts total 2600 pence GBP. No purchase was made."),
]

3. Construct model-call admission

The starter always admits another call. Repair it so call count and configured estimated exposure both stop the loop before another provider attempt. Check the call limit first. Inputs are already validated nonnegative integers.

def decide_admission(case):
    """Return CALL, MODEL_CALL_LIMIT, or MODEL_COST_LIMIT."""
    return "CALL"

Hint 1 — the decision

Admission asks whether the next call may begin. Used attempts never disappear because the previous provider failed.

Hint 2 — the evidence

Inspect used_calls, max_calls, spent, next_cost, and budget. Equality at the money boundary is allowed; exceeding it is not.

Hint 3 — the structure

Return MODEL_CALL_LIMIT when used_calls >= max_calls; otherwise return MODEL_COST_LIMIT when spent + next_cost > budget; otherwise return CALL.

VISIBLE_CASES = [
    ({"used_calls": 0, "max_calls": 3, "spent": 0, "next_cost": 2, "budget": 6}, "CALL"),
    (
        {"used_calls": 3, "max_calls": 3, "spent": 0, "next_cost": 0, "budget": 6},
        "MODEL_CALL_LIMIT",
    ),
    (
        {"used_calls": 1, "max_calls": 3, "spent": 5, "next_cost": 2, "budget": 6},
        "MODEL_COST_LIMIT",
    ),
    ({"used_calls": 1, "max_calls": 3, "spent": 4, "next_cost": 2, "budget": 6}, "CALL"),
]


def grade_admission(candidate, cases):
    rows = []
    for number, (case, expected) in enumerate(cases, 1):
        supplied = copy.deepcopy(case)
        try:
            observed = candidate(supplied)
        except Exception as error:
            observed = type(error).__name__
        rows.append(
            {
                "case": number,
                "expected": expected,
                "observed": observed,
                "status": "PASS" if observed == expected and supplied == case else "FAIL",
            }
        )
    return rows


visible_results = grade_admission(decide_admission, VISIBLE_CASES)
VISIBLE_PASSED = all(row["status"] == "PASS" for row in visible_results)
print(json.dumps(visible_results, indent=2))
print("VISIBLE_CONTRACT", "PASSED" if VISIBLE_PASSED else "NEEDS_WORK")

4. Connect admission to the loop

The loop calls your decision before every model attempt. It increments calls and estimated exposure before invoking the provider, preserves assistant tool requests, invokes the real Chapter 2 dispatcher, and appends identified observations.

def run_loop(
    model, tool_dispatcher, initial_messages, admission, *, max_calls=4, call_cost=2, budget=8
):
    transcript = copy.deepcopy(initial_messages)
    model_calls = 0
    tool_calls = 0
    spent = 0
    seen = set()
    while True:
        decision = admission(
            {
                "used_calls": model_calls,
                "max_calls": max_calls,
                "spent": spent,
                "next_cost": call_cost,
                "budget": budget,
            }
        )
        if decision != "CALL":
            return {
                "status": decision,
                "messages": transcript,
                "model_calls": model_calls,
                "tool_calls": tool_calls,
                "estimated_pence": spent,
            }
        model_calls += 1
        spent += call_cost
        try:
            turn = model.complete(copy.deepcopy(transcript), tool_dispatcher.schemas())
        except ModelError:
            return {
                "status": "MODEL_FAILED",
                "messages": transcript,
                "model_calls": model_calls,
                "tool_calls": tool_calls,
                "estimated_pence": spent,
            }
        identifiers = [call.id for call in turn.calls]
        if len(identifiers) != len(set(identifiers)) or seen.intersection(identifiers):
            return {
                "status": "REPEATED_CALL_ID",
                "messages": transcript,
                "model_calls": model_calls,
                "tool_calls": tool_calls,
                "estimated_pence": spent,
            }
        seen.update(identifiers)
        transcript.append(turn.message())
        if not turn.calls:
            return {
                "status": "COMPLETED" if turn.content.strip() else "EMPTY_REPLY",
                "messages": transcript,
                "model_calls": model_calls,
                "tool_calls": tool_calls,
                "estimated_pence": spent,
                "answer": turn.content,
            }
        for call in turn.calls:
            tool_calls += 1
            result = tool_dispatcher.invoke(call)
            transcript.append(
                {
                    "role": "tool",
                    "tool_call_id": call.id,
                    "content": json.dumps(result, sort_keys=True),
                }
            )


INITIAL_MESSAGES = [
    {"role": "system", "content": "Use stock tools to prepare drafts. Never purchase."},
    {"role": "user", "content": "What needs ordering?"},
]
connected = None
if VISIBLE_PASSED:
    connected = run_loop(
        ReplayModel(OPENING_TURNS),
        dispatcher,
        INITIAL_MESSAGES,
        decide_admission,
    )
    print(connected["status"], connected["model_calls"], connected["tool_calls"])
    print([message["role"] for message in connected["messages"]])
    assert connected["status"] == "COMPLETED"
    assert (connected["model_calls"], connected["tool_calls"]) == (3, 3)
else:
    print("CONNECTION_NOT_READY — repair decide_admission, then run again.")

Trace one tool_call_id from assistant request to tool observation. Final prose alone does not prove the two draft tools returned valid results.

5. Save the bounded transcript

ARTIFACT_PATH = Path("ch03-unit-a-handoff-v1.json")
artifact_status = "NOT_WRITTEN"
if connected is not None:
    encoded = json.dumps(connected, indent=2, sort_keys=True) + "\n"
    ARTIFACT_PATH.write_text(encoded, encoding="utf-8")
    artifact_status = "WRITTEN"
    print(ARTIFACT_PATH)
else:
    print("HANDOFF_NOT_WRITTEN")

Exit ticket

Explain why failed provider attempts count, why call-limit precedence matters when two limits are exhausted, and which transcript observation proves each draft tool actually ran.

exercise_report = {
    "unit": "ch03-a",
    "attempted": 1,
    "completed": int(VISIBLE_PASSED),
    "failed": int(not VISIBLE_PASSED),
    "skipped": 0,
    "connection": "PASSED" if connected else "NOT_READY",
    "handoff": artifact_status,
}
print("EXERCISE_REPORT=" + json.dumps(exercise_report, sort_keys=True))

Changed-constraint construction: Admit a variable-cost next model attempt

Allow twenty minutes. Spend three minutes predicting, ten implementing and tracing, five on a new case of your own, and two explaining the surviving limitation. This is dedicated work, not an invitation to run a supplied answer. Both units revisit the same invariant after different core experiences; in Unit B, attempt this task from memory before consulting Unit A.

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.

Write your expected values before running the table. Keep one accepted case and one refusal. Your function is passed directly into the driver below. The driver copies inputs and checks they remain unchanged; it does not replace your implementation with the reference answer.

Hint 1 — identify the authoritative inputs Name the source field for each output value. Which input changes while the rule remains the same?

Hint 2 — choose the boundary cases Start with exact empty, exact equality and one value on each side of the boundary where valid. Do not add a special case for a visible product name or operation identity.

def transfer_check(state):
    raise NotImplementedError("Decide before admitting the next attempt")

import json

TRANSFER_CASES = [
    ("exact fit", [{"calls": 1, "max_calls": 3, "spent": 4, "next_cost": 3, "budget": 7}], "CALL"),
    (
        "next attempt too costly",
        [{"calls": 1, "max_calls": 3, "spent": 4, "next_cost": 4, "budget": 7}],
        "COST_LIMIT",
    ),
    (
        "two exhausted limits",
        [{"calls": 3, "max_calls": 3, "spent": 7, "next_cost": 1, "budget": 7}],
        "CALL_LIMIT",
    ),
    (
        "zero allowed attempts",
        [{"calls": 0, "max_calls": 0, "spent": 0, "next_cost": 0, "budget": 0}],
        "CALL_LIMIT",
    ),
]


def same_transfer_value(actual, expected):
    if type(actual) is not type(expected):
        return False
    if isinstance(expected, dict):
        return actual.keys() == expected.keys() and all(
            same_transfer_value(actual[key], value) for key, value in expected.items()
        )
    if isinstance(expected, list):
        return len(actual) == len(expected) and all(
            same_transfer_value(a, e) for a, e in zip(actual, expected, strict=True)
        )
    return actual == expected


def run_transfer(candidate, cases):
    observations = []
    for label, arguments, expected in cases:
        supplied = copy.deepcopy(arguments)
        before = copy.deepcopy(supplied)
        raised = None
        try:
            actual = candidate(*supplied)
        except NotImplementedError:
            raised = "NotImplementedError"
            actual = {"unfinished": True}
        except Exception as error:
            raised = type(error).__name__
            actual = {"raises": raised}
        expects_error = isinstance(expected, dict) and set(expected) == {"raises"}
        correct = (
            raised == expected["raises"]
            if expects_error
            else (raised is None and same_transfer_value(actual, expected))
        )
        passed = correct and same_transfer_value(supplied, before)
        observations.append(
            {"case": label, "expected": expected, "observed": actual, "passed": passed}
        )
        print("PASS" if passed else "NEEDS_WORK", label, "expected", expected, "observed", actual)
    return observations


transfer_observations = run_transfer(transfer_check, TRANSFER_CASES)
TRANSFER_PASSED = all(row["passed"] for row in transfer_observations)
print("TRANSFER_STATUS", "PASS" if TRANSFER_PASSED else "NEEDS_WORK")

Design a counterexample and retrieve the mechanism

Add one new case with an independently calculated expected outcome to TRANSFER_CASES and rerun the driver. Change one condition at a time. Then deliberately replace your candidate with a constant answer in a temporary copy and show a case that rejects it. Restore your implementation. Explain why that counterexample is stronger than repeating the original example with a new name.

Without viewing the worked example, write the invariant in words and trace one observed value back to its input. Identify which part is a local fixture result and which claim would need a live provider, host or external-system observation. Keep a first attempt even if you used a hint.

Save your evidence and explain the result

Fill the prediction notes and your explanation before saving. Include the exact observed value, the input or retained row that caused it, your code's invocation point, one failed hypothesis, and the strongest claim the evidence still cannot support. A completed code cell alone does not earn explanation credit. Do not label reference-start behavior as your own Unit A construction.

Keep this edited notebook, the Markdown if used for notes, saved handoff files, and the JSON record below. Your work folder survives scratch cleanup and can be reopened in a new kernel. An instructor can ask for an unseen case after the visible checks; keep your implementation general.

explanation_notes = {
    "causal_trace": "Explain the input, learner invocation and observed result.",
    "failed_hypothesis": "Describe a prediction the evidence changed.",
    "remaining_limit": "Name the guarantee not established by this experiment.",
}
course_submission = {
    "unit": "ch03-a",
    "planned_minutes": 90,
    "starting_evidence": globals().get("HANDOFF_ORIGIN", "INDEPENDENT_UNIT_A"),
    "prediction": prediction_notes,
    "explanation": explanation_notes,
    "core_report": exercise_report,
    "transfer": transfer_observations,
    "explanation_review": "HUMAN_REVIEW_REQUIRED",
}
submission_path = COURSE_WORK / "ch03-a-submission-v1.json"
submission_path.write_text(
    json.dumps(course_submission, indent=2, sort_keys=True), encoding="utf-8"
)
print("Saved evidence:", submission_path)
print(
    "COURSE_REPORT="
    + json.dumps(
        {
            "unit": "ch03-a",
            "transfer_passed": TRANSFER_PASSED,
            "starting_evidence": course_submission["starting_evidence"],
            "edition": "student",
        },
        sort_keys=True,
    )
)

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