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Advanced Loop Engineering

Define how a loop finishes, how it recovers from failure, and whether it is worth running

开始学习
全部章节
8
Locale
English
课程类型
开放课程

Design and review task loops that keep running over time. Define task state and completion evidence, set tool permissions and budgets, and use execution records to decide when to continue, stop, or recover. Test whether changes to the rules make the process more reliable.

适合人群

  • People with experience using AI, scripts, or workflows who need to design or review task loops
  • People who need to manage long-running tasks, event-triggered work, or collaboration among several roles
  • People responsible for deciding whether to expand a loop’s use based on quality, risk, and cost

解决的问题

  • A task repeats, but its state, next step, and stop conditions are unclear
  • A task is declared complete based only on metrics or the executor’s own assessment
  • Tool access and permissions are too broad, with no approval process or recovery plan for key actions
  • Events, queues, and roles keep being added without a clear reason
  • Fixes address the current result, but rework and operating costs keep growing

    • 学完收获

    • Use states, events, and transition conditions to describe normal operation, failure paths, and termination
    • Choose tests, evaluations, reviews, or human approval for completion checks, and explain what each can establish
    • Match tools, permissions, isolation, and approvals to the task, and check what state must be saved for recovery
    • Choose a runtime pattern based on triggers, duration, and control needs, and handle duplicate events and overlapping runs
    • Trace a failure through the run records, choose a recovery method, and propose rule changes you can test and undo
    • Estimate total cost and weigh quality, safety, and budget when deciding whether to pilot, expand, narrow, or stop the loop

    课程亮点

    • Eight lessons cover loop models, verification, execution limits, operation, recovery, costs, and rule improvements
    • AI agents are the main example; the methods also apply to scripts, workflows, and human collaboration
    • Practice reviewing decisions with evidence tables, failure records, and cost estimates
    • Weigh the benefits and maintenance costs of reviewers, parallel branches, and task queues

    全部章节

    8 个章节

    Lesson 1: Understand How an Agent Runs and Stops

    Use states, events, and transition conditions to explain how a task loop starts, advances, changes direction, and stops.

    Lesson 2: Define When an Agent Is Done

    Turn a vague request into a task goal that can be checked, then use constraints, evidence, and terminal states to decide when the task is complete or must stop.

    Lesson 3: Use Evidence to Verify an Agent Is Really Done

    Connect criteria, evidence, decision owners, and conclusions, then explain what each kind of evidence can and cannot prove.

    Lesson 4: Configure Tools, Permissions, and Isolation for an Agent

    Set tools, permissions, isolation, context, state, credentials, and approvals from task needs, then judge whether the execution environment is capable, safe, and recoverable.

    Lesson 5: Choose When and How an Agent Runs

    Choose the simplest runtime pattern that fits how long a task lasts, what triggers it, how it is verified, and what risks and costs it carries.

    Lesson 6: Diagnose and Recover When an Agent Fails

    Separate symptoms, direct failures, and systemic root causes from runtime records, trace failures through the state chain, and use evidence to choose retry, strategy change, degradation, blocking, or human takeover.

    Lesson 7: Use Cost and Runtime Data to Decide Whether to Scale

    Use total cost and runtime data to decide whether a loop should continue, stay in a pilot, scale, run less often, narrow its scope, or be retired.

    Lesson 8: Turn One Failure into Lasting Improvement

    Turn one runtime incident into an evidence-based, versioned, tested, and reversible improvement, then decide when a rule should be released, merged, or retired.