Anthropic published “The components of agent memory” on November 6, 2026, describing a practical architecture for retaining knowledge across agent sessions. It combines transcripts, memory notes, structured representations such as wikis, and a small amount of context loaded into future sessions. An offline process called “dreaming” checks and reorganizes those layers.
This is implementation guidance backed by evaluations, not a new Claude model or consumer feature. The useful finding is that memory quality depends on what an agent preserves, how it validates that information, and when it retrieves it. Saving more material can leave an agent with stale facts, repeated mistakes and less room to work.
Four Memory Layers With Different Jobs
Anthropic defines agent memory as knowledge gained from experience and retained between sessions as readable context. That excludes changes to model weights, learning within a single conversation, and context compaction, which summarizes older turns so an ongoing task can continue.
The architecture separates the record of what happened from the information worth carrying forward:
| Layer | What it contains | Practical role |
|---|---|---|
| Transcripts | Session activity and interaction history | Preserve searchable evidence of what happened |
| Memory notes | Selected facts, corrections and rules | Carry useful lessons between sessions |
| Representations | Organized material such as linked wikis | Connect information across notes and documents |
| Injected context | A small selection loaded into new sessions | Make essential guidance immediately available |
Anthropic’s guide describes dreaming as an offline process that can correct mistakes, create notes, build representations or change what gets loaded next time.
The distinction matters. A transcript provides evidence, but it can be expensive to search repeatedly. A note makes a conclusion easy to reuse, but it can preserve a mistaken interpretation. A wiki connects related material, while injected context makes selected information unavoidable in every session.
My reading is that these layers should have different admission standards. Keeping an event in a searchable archive is a modest commitment. Putting its conclusion into every future session gives that conclusion much more influence, so it deserves stronger validation.
Persistent Memory Helps When Models Validate Their Rules
Anthropic tested a database task from Continual Learning Bench, or CL-Bench, 1.0 using Claude Managed Agents. Each question began in a fresh session. A persistent memory directory was the exception: agents could retrieve files saved during previous questions.
After answering, the model received the correct solution and could write a memory for later use. That creates an opportunity to turn feedback into reusable rules.
Anthropic reported the following results, detailed in Subagentic’s account of the evaluation:
| Model | Stateless mean reward | With persistent memory |
|---|---|---|
| Claude Sonnet 4.6 | 0.330 | 0.364 |
| Claude Sonnet 5 | 0.306 | 0.610 |
| Claude Opus 4.7 | 0.383 | 0.700 |
| Claude Opus 5 | 0.564 | 0.828 |
| Claude Sonnet 5.5 | 0.644 | 0.833 |
| Claude Opus 5.5 | 0.655 | 0.838 |
These are Anthropic-run evaluations, with no independent reproduction in the supplied sources. Their results depend on the model, task, memory instructions, search tools and offline-compute budget.
Opus 5.5 gained 0.183 in mean reward. Sonnet 4.6 gained only 0.034 from having a memory directory available. The differences in their notes help explain why a storage mechanism alone is insufficient.
According to Anthropic, Sonnet 4.6 recorded scattered, untested theories. Opus 4.7 distinguished checked facts from unconfirmed ones. Opus 5 recorded rules, evidence supporting those rules, previous answers and warnings about discovered traps. Opus 5.5 and Sonnet 5.5 also completed that feedback loop.
The lesson for builders is specific: evaluate whether a model turns corrections into validated guidance. File-writing ability is a weak proxy for that skill.
Instructions also changed the outcome. Restricting memory to failures reduced gains for several models. More explicit instructions helped Sonnet 4.6: prompting it to read its memories and test its theories raised its score to 0.641, according to the detailed results. Stronger models needed less steering. I’d treat memory prompts as something to evaluate per model, especially after a model upgrade.
A Large Memory Can Become Another Task to Manage
Anthropic’s Pokémon Red experiments provide a concrete counterexample to the idea that more saved information means better memory.
At roughly 10,000 game steps, one Sonnet 3.7 run had created 413 files across 225 folders and earned one badge. A Fable 5 run used 10 files and earned four badges. These were individual runs, so the comparison illustrates memory behavior without isolating memory size as the cause of game performance.
Sonnet 3.7 saved extensive information about battles, items and characters. Much of it became stale as the game changed. At one point, it spent 17 steps creating and deleting memory files without taking an action in the game.
Fable 5 concentrated on information the current screen could not supply: goals, route notes, gym plans and tested rules. The account of Anthropic’s comparison makes the tradeoff clear: maintaining an archive can consume the effort that should advance the task.
For builders, the useful distinction is between durable knowledge and recoverable state. If a tool can cheaply show the current inventory or location, repeatedly preserving that state may add little. A verified rule about an unfamiliar environment is harder to recover and more valuable to retain.
Dreaming Can Repair a Confidently Wrong Note
Offline consolidation addresses a different problem: an agent can select an important fact and still record it incorrectly.
In Anthropic’s Pokémon example, Claude saved an incorrect PokéCenter location around Mt. Moon and concluded it had already crossed the cave. Later navigation reinforced that mistaken interpretation. The agent repeatedly dropped onto an earlier route while trusting its notes.
A dreaming pass checked the memory against the session transcript and corrected the location. With the corrected note, Claude reached Cerulean City at action 1,935. The uncorrected run repeated the navigation failure and eventually stopped, according to the published account.
The important mechanism is evidence checking. Shortening the original note would have retained the same error. Reviewing what actually happened gave the offline process a chance to challenge it.
That is a strong reason to retain searchable transcripts even when the working memory is compact. A source reference attached to a durable rule gives a later consolidation pass something concrete to inspect. The benefit is grounded in this example; the broader recommendation is an architectural inference from it.
Search and Wikis Worked Better Together
Anthropic also tested a generated wiki over an internal corpus of about 350,000 files, containing roughly 420 million tokens. Opus 5 agents used between two and 128 sessions to create and improve linked pages, merge duplicates and check claims.
On 25 evaluation questions:
- Wiki alone rose from 13% to 48% of graded criteria as the offline work increased.
Frequently Asked Questions
3 questions
1Is This a New Claude Model Release?
No. Anthropic published implementation guidance, evaluations and supporting cookbooks for agent memory. The post describes architecture and tradeoffs.
