State, Events and Recoverable Execution

Why a Conversation Log Is Not Execution State

Chapter I establishes tools input and authorization, which in turn transforms the execution history into a recyclable object. Chat text may write “Saved”, with no response; or the tool is completed and the model cannot be exported. To restore the system, it is necessary to identify the identity of the mission, the cause of the current termination, the observed results, the actions yet to be performed, cumulative attempts, strategic decision counts and outcomes. These fields together constitute the status s. The natural language interpretation in the log can be readable, but it must not serve as the only basis for determining whether the inventory has changed. The project is saved by structured JSON status and SQLite, rather than from the chat text guessing phase.

The task id is a restored identity, not a display of title. The same id must be re-started with the same field as the original task; if the number or permission changes, a new identity should be used, otherwise recovery will mix two different business intentions into one thing. Project start first reads the original snapshot, compares the task, and then resets the event chain. New tasks are created when there is no status. The end state re-runs only back to existing status and does not redo tools; this is different from blindly starting again, which may repeat the reservation.

The Smallest Unit of a State Transition

The active state includes at least task, status, observations, pending, caps, decisions, attachments, result. Observations save confirmed results by toolbar name;pending saves the full pending action and cannot save only the tool name, otherwise the parameters or buttons of restoration may be regenerated. Calls is the total number of actual tool attempts and failures are counted; Attemps are the current attempts at sending; decisions are the number of tool actions that are prepared for local JSON strategy to locate the next action. Three counts answer different questions and cannot replace each other.

A normal tool action starts with three sustainable boundaries: prepared, attached_started, and observed. Prepared first saves the policy selection and complete parameters; attempt_started increases the number of attempts to be counted and saved before calling; observed adds observations and emptys the feeding after the result returns. This allows the middle state of “Attempts recorded but not yet effected” and the middle state of “Effect has been submitted but observation not saved”. The latter requires a Chapter 4 account to be processed in such a way that this chapter does not pretend to save a state only to guarantee long-range side effects just once.

The Roles of Event Chains and Snapshots

Quick images allow recovery to quickly achieve the latest state, and the event chain allows readers to verify how it reaches. Each time the project saves an event and updates the snapshot in the same SQLite service, the event contains a type and the complete state. The task-based maintenance numbering starts from zero, records a summary of the previous event, and calculates SHA256 for numbers, presumes and loads. Preservation of the full state takes more space than a mere memory increment, but it facilitates resetting; real systems can use a tighter snapshot of events and cycles, provided that the reconstruction results are still validated to match the current snapshot.

Standardization avoids that the same dictionary produces different summaries simply by type of key order. Items are sorted, separated closely and created by keeping Chinese code. Re-selling item reconciliation numbers is continuous, previous, summaries are identical, and final comparison of final event status with a states watch snapshot. This check may detect deletions of intermediate events or changes only snapshots. It is not a cryptographic audit system that is tampered with by the administrator: a person who can rewrite all events and summaries at the same time can still forge a new chain, and if the threat is resisted, an external anchor or signature must be added.

Worked Example 1: Stopping the Process After Two Tools

In the case of reserve-camera, the initial status calls zero and observational empty. After executing the search calls hold only hits; after executing document calls holds both text and content summaries. At this point, stop_after=2 returns ranning, the database has two steps of observation. Closes the connection and re-opens the same database and loads the same task. The policy reads that there is already a search and document, only stock, then reserve, and finally ends. The full state of restoration is the same as uninterrupted operation, including call times four and final inventory two; it does not compare only “Success” text.

import pathlib,tempfile,json
from agent import Store,Runner
from run import ROOT
row=json.loads((ROOT/'data/tasks.jsonl').read_text().splitlines()[1])
with tempfile.TemporaryDirectory() as td:
    path=pathlib.Path(td)/'state.sqlite'
    store=Store(path,ROOT/'data');store.import_documents()
    partial=Runner(store).run(row['task'],stop_after=2)
    assert partial['calls']==2 and partial['status']=='running'
    store.db.close()
    store=Store(path,ROOT/'data')
    final=Runner(store).run(row['task'])
    assert final['status']=='reserved' and final['calls']==4
    assert store.replay(row['task']['id'])==final
    store.db.close()
print('resume PASS')

Worked Example 2: What Snapshot-Only Storage Misses

Assuming that normal events are zero, then there is prepared and observed. Some simply modify the status forms to reservated, but no reserve observes or echoes. If you restore a snapshot, the fictional results will be completed. The project replay will find the last event not equal to the snapshot. Conversely, modifying the event payload without updating the summary will fail in the event. Tests the actual implementation of SQL rewrite loads and requires to drop the broken event log instead of checking the log with the naked eye.

There is also a more common error: duplicate numbers or jump numbers. The main key of the project prevents the insertion of the same task number twice, and reloading the list again equals the actual order starting from zero. Each task has its own numbering chain and should not require a strict staggering of the entire database event. Since only single-work processes are supported at this time, multiple process grabs and leases have not been achieved; the contest driven simultaneously by two workers is out of the receiving and inspection range. Database services provide a partial atomic character but cannot automatically supplement and grant ownership over any arbitrary application of the protocol.

A Successful Save Does Not Confirm the Side Effect

If the process is interrupted after attachment_started and before it is actually called, it is reasonable to see the pending again; if it is interrupted after tool matter has been submitted and before observed, the same pending will also be retested. The client end state alone cannot determine whether the same status is valid from both appearances. The project thus perpetuates the tool 's endpoint etc. and resumes reading the results when retrying with the same key. This is at the core of cross-border uncertainty, and “Every step of the way.” should not be misspelled as automatically avoiding all duplications. Chapter IV will be deduced from time to time.

The first category consists of path equivalents: stop at multiple cut points of zero to four steps and recover against completeness, stock and compliance. The second category consists of damage detection: change of mission content, change of event load, change of snapshot, or change of source version cannot continue silently. The former proves that the usual legal interruptions can be restored, while the latter test system does not treat inconsistent objects as a normal history. “File Exists After Memory” The dictum does not cover these semantics. The current test is performed on a real SQLite file, without a fake memory dictionary simulation.

import hashlib,json
def hashed(obj):
    raw=json.dumps(obj,sort_keys=True,separators=(',',':')).encode()
    return hashlib.sha256(raw).hexdigest()
previous='0'*64
first={'seq':0,'previous':previous,'payload':{'status':'running'}}
h0=hashed(first)
second={'seq':1,'previous':h0,'payload':{'status':'info'}}
assert second['previous']==hashed(first)
changed={**first,'payload':{'status':'reserved'}}
assert second['previous']!=hashed(changed)
print('chain and mutation PASS')

Exercises and Project Contributions

This chapter is delivered by Store.save, snapshot, replay and breakpoint Runner. The next chapter considers the strategy as a replacement unlettered action proposer, specifying when to continue and when to stop; status and log are traceable, so replacing the decision source will still use the same tools and recovery checks.

# LearnerRunner.start（替换已提供类的此方法）
def start(self,task):
        raise NotImplementedError('state')

# LearnerRunner.start（替换已提供类的此方法）
def start(self,task):
        validate_task(task);old=self.store.snapshot(task['id'])
        if old:
            if old['task']!=task:raise ContractError('task ID reused with different request')
            self.store.replay(task['id']);return old
        state={'task':task,'status':'running','observations':{},'pending':None,'calls':0,'decisions':0,'attempts':0,'result':None}
        self.store.save(state,'created');return state