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Guide multi-source research through source checks, comparison of key claims, and recovery from failed tool calls. Produce reports that distinguish retrieved evidence from unresolved questions and clearly document remaining gaps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking key claims against multiple sources
• Recovering from failed source retrieval
• Documenting evidence and gaps in research reports

Design systems where multiple agents work with separate task information. Choose coordination structures, define handoffs and shared state, and plan parallel work while checking whether communication costs and failure risks justify using more than one agent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Split large tasks across separate agent contexts
• Run independent research tasks in parallel
• Define handoffs between specialized agents

Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare responses from different models
• Run A/B tests for prompt changes
• Create shared scoring rubrics

Design file-based storage for agent context, including large tool results, plans, logs, and handoff notes. Keep information available outside the conversation and retrieve relevant sections when needed, instead of loading everything at once.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving large tool results for selective reading
• Keeping plans available during long tasks
• Sharing findings between agents through files

Turn ePub books into training examples for models that reproduce an author's writing style. Covers text extraction, coherent segmentation, varied instructions, model training, and checks for copied phrases and style transfer to new scenarios.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build training datasets from ePub books
• Train a model on an author's writing style
• Check for copied phrases in generated prose

Model what an agent believes, wants, and commits to doing. Connect external facts to goals and plans using structured relationships, with evidence and validity periods that help trace why the agent chose an action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert RDF data into agent beliefs
• Trace the reasons behind agent decisions
• Track belief validity and changes over time

Design evaluation systems that measure agent results across accuracy, completeness, citations, and tool efficiency. Build repeatable tests, compare changes against a baseline, and define quality checks that catch regressions before deployment and monitor production performance.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare agent configurations against a baseline
• Catch quality regressions before deployment
• Measure the effects of context strategies

Design structured summaries for long agent sessions that preserve goals, file changes, decisions, and next actions. Choose compression methods and timing, then test whether the agent can continue accurately without repeatedly retrieving information it already processed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Summarize long coding sessions for continuation
• Preserve file changes and decision history
• Create durable summaries for task handoffs

Diagnose why an AI agent becomes less reliable as conversations and documents accumulate. Distinguish missed information, persistent false claims, irrelevant material, mixed tasks, and conflicting sources, then choose ways to clean, reorganize, or separate the context.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating declining quality in long conversations
• Recovering from false claims that persist after correction
• Resolving conflicts between retrieved documents

Learn how instructions, documents, tool results, and conversation history form an AI model’s context. Understand attention limits, why information in the middle can be missed, and how relevance and placement inform decisions about what to include.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learning the basics of context engineering
• Understanding why more input does not always help
• Explaining how instruction placement affects attention

Plan how an AI agent uses limited input space through reusable prompt prefixes, shorter tool outputs, summaries, and task splitting. Measure token use, response quality, and latency to choose optimizations that provide a net benefit.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing token use from lengthy tool outputs
• Improving prompt cache reuse
• Planning context budgets for long conversations

Design infrastructure for agents that run in isolated remote environments. Plan faster session startup, saved work states, shared sessions, authentication, and delivery of code changes and other results back to users.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing infrastructure for background coding agents
• Reducing delays when agent sessions start
• Saving and restoring session work

Design the controls around autonomous agents: what they can edit, how results are evaluated, how progress is logged, and when human approval is required. Build research and experiment loops with clear recovery and review rules.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing autonomous research and experiment loops
• Separating editable work from fixed evaluation rules
• Preserving progress and failed attempts across long runs

Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing repeated history sent to worker agents
• Comparing internal memory sharing with text summaries
• Assessing runtime support for KV cache compaction

Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning vague requests into precise task briefs
• Defining stopping rules for long autonomous runs
• Specifying deliverables for parallel workers

Design persistent agent memory that retains knowledge across sessions and retrieves relevant facts when needed. Compare storage approaches, track people and changing information, and plan memory consolidation and evaluation based on the kinds of questions the agent must answer.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retain user preferences across conversations
• Track people and entities consistently across sessions
• Retrieve facts valid at a specific point in time

Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether a task is suitable for an LLM
• Designing a pipeline for batch analysis
• Estimating processing costs before development

Design loops that improve an agent’s own prompts, workflows, or execution code from failure records. Define bounded edits, preserve candidate histories, and accept changes through independent tests that check improvements and regressions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing agent self-improvement from failure records
• Defining acceptance checks for self-modifying workflows
• Diagnosing score gaming and stalled improvement

Design how an AI agent edits its own working conversation history, choosing what to retain, rewrite, or remove. Covers safeguards, capacity monitoring, recovery after overflow, and the computing cost of changing earlier context.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing agents that edit their own conversation history
• Preserving exact values during long tasks
• Recovering from context overflow

Design tools that agents can select and call correctly. Clarify descriptions, input rules, response formats, and recovery messages, then review overlapping tools and naming conventions using examples of successful and failed calls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Writing descriptions and input rules for new tools
• Investigating incorrect tool selection and calls
• Improving error messages with recovery steps

Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Use a structured starting point to write new context engineering skills. Organize activation conditions, responsibilities, practical steps, examples, and common mistakes, while keeping detailed references separate and clarifying boundaries with related skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Draft a new SKILL.md
• Clarify when a skill should activate
• Define boundaries with related skills

Organize content, contacts, knowledge, and goals in structured files. Use recorded writing preferences and past posts to draft content, prepare meeting briefs from relationship history, and review weekly progress.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting posts in a consistent personal voice
• Preparing meeting briefs from contact history
• Reviewing weekly progress and goals

Analyze records of an AI agent’s decisions and tool use to find repeated failures, lost goals, and misunderstood instructions. Use the findings to refine prompts, rerun tasks, and turn debugging lessons into reusable skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating repeated agent failures
• Reviewing incorrect tool choices
• Refining prompts through repeated evaluation