# What the model sees _Thread · sourced_ One question followed across the site: what is in front of the model this turn, and who decided to put it there. The answers run from a written instruction to a note a session leaves for the next one. ## Guided worked example · Business & team operations Fictional scripted fixture, not a measured run. No model or external actions execute. **Overview:** Follow the information available at a particular step and compare it with the wider task history. Inspect what changes after truncation, retrieval, or summarization. **Assumptions:** The model cannot rely on details that are absent from its current input unless another mechanism retrieves them. A summary may drop unresolved conditions. **Design choices:** Keep decision-critical constraints, provenance, and open questions when compressing context. Retrieve detailed evidence when a later step depends on it. **Request:** Resume an investigation after conversation summarization. **Starting evidence:** Summary: warranty question pending. Omitted: use manual v3, not v1. Both files exist. **Action and control:** Inspect the actual next request and restore the missing constraint and evidence. **Stage records (authored, not executed):** ### Input record Summary: warranty question pending. Omitted: use manual v3, not v1. Both files exist. What changed: Establish the facts supplied for this version of the task. ### Design note Keep decision-critical constraints, provenance, and open questions when compressing context. Retrieve detailed evidence when a later step depends on it. What changed: Choose an approach before treating a proposed result as accepted. ### Proposed work Inspect the actual next request and restore the missing constraint and evidence. What changed: Turn the request and evidence into the next action or transformation. ### Result record · illustrative Context now includes the question, v3 requirement, and applicable passages. Unloaded files previously contributed nothing. What changed: Inspect the result of the authored example; this is not an executed model run. ### Verification plan Before/after context trays, omitted evidence, restored constraints, and a corrected supported answer. If the result falls short: If a resumed task contradicts prior decisions, inspect the assembled context and restore missing state. Do not assume the full conversation was available. What changed: Separate what needs checking from what the illustration establishes. ### Adaptation handoff Use this for long conversations, project assistance, or recurring work. Decide what must persist explicitly and what can be recovered on demand. What changed: Decide which assumptions, tools, and controls should change for your own task. **Sample result:** Context now includes the question, v3 requirement, and applicable passages. Unloaded files previously contributed nothing. **Change something — Assume saving a file makes it visible:** The request still lacks its contents. Storage does not automatically supply context. **Decision:** Does saving a file ensure the next model call sees it? **Answer:** No; verify relevant content is loaded. **Why:** Saved memory or files do nothing unless loaded; instructions, retrieved documents, and tool results have different roles. **Review criteria:** Before/after context trays, omitted evidence, restored constraints, and a corrected supported answer. **Recovery:** If a resumed task contradicts prior decisions, inspect the assembled context and restore missing state. Do not assume the full conversation was available. **Adapt it:** Use this for long conversations, project assistance, or recurring work. Decide what must persist explicitly and what can be recovered on demand. > A model knows what it was trained on and what is in this request; everything else is a choice somebody made. For a normal inference request, a language model works from learned parameters and the supplied context. Files, past conversations, and tool results become available through the surrounding system. Ask what is stored, who selects it, and what is actually included now. ## Choose information at the right boundary | Mechanism | What it contributes | What you still decide | |---|---|---| | Prompting | Instructions, examples, output contract | What success means | | Context assembly | Material supplied for this call | Relevance, order, access, budget | | Retrieval | Selected material from a collection | Search, source identity, evidence coverage | | Memory | Information retained for later use | What to store, update, expire, or delete | | Skills | Reusable procedures and resources | When to load them and their authority | | Checkpoints | State across sessions | What is durable and how to resume | ## Four distinctions to keep straight **Retrieval selects material, not necessarily truth.** Sources can contain instructions, errors, obsolete policy, or malicious text. Distinguish source material from instructions that govern the task. **A summary can lose a qualification.** Keep a route back to original records when omitted detail could change the answer. Summaries and structured records can coexist. **Memory is useful within sessions too.** A long conversation can use external state. Applications can save task records deterministically, and agents at several levels can select what to remember. Model-directed memory is not exclusive to level 7. **A note is not the whole environment.** Resuming work can require files, tool state, action receipts, permissions, and unfinished-task records. Restore and inspect them before relying on the last summary. ## Try a revealing case Supply a current policy and a conflicting remembered fact. Does the system notice the conflict and preserve provenance? Remove the relevant source. Does it admit the gap? Relevant, sufficient context is the target. More tokens can add cost and distractions; a large context window does not guarantee useful attention to every fact. Caching behavior and pricing vary by provider. ## Sources 1. [Effective harnesses for long-running agents](https://www.anthropic.com/engineering/effective-harnesses-for-long-running-agents) — Anthropic (accessed 2026-09-20) ## Pages that carry it - [Prompt engineering](/gradient_ascent/techniques/prompt-engineering/) (sourced): Writing instructions that get consistent results. - [Context engineering](/gradient_ascent/techniques/context-engineering/) (sourced): Deciding what goes into the request, and caching the parts that repeat. - [Retrieval-augmented generation (RAG)](/gradient_ascent/techniques/rag/) (measured): Searching your documents and giving the results to the model. - [Memory](/gradient_ascent/techniques/memory/) (sourced): Keeping information from one conversation to the next. - [Skills](/gradient_ascent/techniques/skills/) (sourced): Reusable instructions that an agent loads when it needs them. - [Long-running tasks](/gradient_ascent/techniques/long-horizon/) (sourced): Tasks that run for hours or days. Last reviewed 2026-09-20.