CONTEXT ENGINEERING
Memory Health Protocol
Why AI forgets – and how to fix it
In 30 Seconds
AI doesn't remember. Every conversation starts fresh. Every session begins from zero. The brilliant assistant who helped you yesterday has no idea who you are today.
This isn't a bug – it's how LLMs work. But it's also why most AI implementations deliver inconsistent value. The forgetting problem is solvable.
Memory health is the practice of designing systems that give AI what it needs to know – when it needs to know it – without drowning in irrelevant information.
Why This Matters
For Individuals
Without memory, you re-explain context every session. The same background, the same preferences, the same project details – again and again.
Time saved by AI gets consumed by context-setting. The productivity promise erodes with every fresh start.
For Teams
When AI forgets, knowledge doesn't compound. Insights from one session don't inform the next. Each team member starts from scratch.
The result: inconsistent outputs, duplicated effort, and AI that never gets better at understanding your work.
The compounding cost: Every time AI forgets, you lose the value of everything it learned. Good memory design means knowledge builds over time instead of resetting to zero.
The Forgetting Problem
Understanding why AI forgets is the first step to fixing it
Context Windows Have Limits
Every AI model has a finite “context window” – the amount of text it can consider at once. When the window fills, old information gets pushed out.
The Illusion
Modern models have large context windows (100K+ tokens). This feels like plenty of memory.
The Reality
Long contexts degrade performance. Research shows accuracy drops 14-85% as context length increases – even with relevant information.
No Native Persistence
LLMs have no built-in way to store information between sessions. Unlike databases or file systems, they don't write to permanent storage. Each conversation exists in isolation.
What Users Expect
“You remember that project we discussed last week, right?”
What Actually Happens
The model has no access to previous conversations. Last week doesn't exist.
Lost in the Middle
Even within a single context window, attention isn't uniform. Models favour information at the beginning and end, often missing what's in the middle.
The Pattern
Critical information buried in the middle of long conversations gets less attention from the model.
The Impact
Important context can be effectively “forgotten” even while technically still in the window.
Key insight: The “memory problem” isn't a flaw to be fixed by model improvements. It's a fundamental architecture that requires system-level solutions.
Symptoms of Poor Memory Health
Recognise these patterns? They're signs your AI system needs memory architecture.
Repetitive Context-Setting
You explain the same background information every session. “I work at X company, we do Y, the project is about Z...”
Inconsistent Outputs
The same question yields different answers in different sessions. No learning from previous interactions carries forward.
Contradictory Advice
AI suggests approaches that conflict with decisions made in previous sessions. It doesn't know what was already decided.
Context Rot
Long conversations degrade. The AI starts referencing outdated information or losing track of earlier agreements.
Knowledge Silos
Insights from one conversation can't be applied elsewhere. Each session is an island of learning that sinks after use.
The Eternal Beginner
Despite months of use, AI still asks basic questions. It never develops understanding of your domain or preferences.
These aren't AI limitations. They're architecture gaps. Every symptom has a solution – if you design for memory.
The Four Memory Strategies
Based on Anthropic's context engineering framework
1. Write: Persist Externally
Since AI has no native memory, create external storage. Files, databases, knowledge bases – anything that persists beyond the session.
Session Logs
Capture key decisions and outcomes from each conversation
Knowledge Files
Curated information that AI should always know
State Documents
Living files that track current project status
2. Select: Load Only What's Relevant
Don't load everything every time. Retrieve context based on the task at hand. Just-in-time loading beats all-the-time loading.
Dynamic Retrieval
Fetch relevant documents based on the current query
Semantic Search
Find information by meaning, not just keywords
Role-Based Loading
Different tasks load different context packages
3. Compress: Summarise, Don't Accumulate
Keep context lean. Replace long conversation history with concise summaries. Archive old content, preserve decisions, trim the unnecessary.
Conversation Summaries
Replace 50 messages with 5 key takeaways
Decision Logs
Keep what was decided, not how it was discussed
Context Pruning
Regular maintenance to remove outdated information
4. Isolate: Separate Contexts for Separate Concerns
Don't let different workstreams pollute each other. Use boundaries to keep contexts clean and focused.
Session Boundaries
Clear starts and ends for different work types
Project Isolation
Client A's context doesn't leak into Client B
Multi-Agent Design
Different agents with different specialised contexts
Layered Memory Architecture
Organise memory by stability and scope
Effective AI memory isn't a single file – it's a layered architecture. Higher layers are stable and rarely change. Lower layers are ephemeral and session-specific.
The Design Principle
Load stable layers automatically. Load ephemeral layers dynamically. Don't burden every session with information that rarely changes.
The Maintenance Principle
Update each layer at appropriate intervals. Strategic memory weekly. Session context every message. Match maintenance rhythm to layer stability.
Memory Patterns in Practice
Common patterns for implementing healthy AI memory
The Handoff Document
A single file that captures: what happened, what was decided, what's next. Updated at session end, loaded at session start.
Best for:
Individuals working across multiple sessions on the same project
The Project Bible
A comprehensive reference document containing all project context. Loaded automatically when working on that project.
Best for:
Complex projects with many decisions and constraints to remember
The Skills Library
Modular knowledge files that can be loaded on demand. Different skills for different tasks, loaded as needed.
Best for:
Teams with diverse tasks requiring different domain expertise
The Weekly Bridge
A rhythm-based summary that synthesises the week's sessions. Carries forward key context without accumulating endless history.
Best for:
Ongoing operations with continuous but evolving context
Memory Requires Maintenance
Without Maintenance
- • Context files grow stale
- • Outdated information contradicts current reality
- • Memory becomes noise rather than signal
- • AI references things that are no longer true
- • The system degrades back to forgetfulness
With Maintenance
- • Context stays current and accurate
- • Old information gets archived, not deleted
- • Each session starts with relevant, fresh context
- • Knowledge compounds reliably over time
- • The system gets smarter, not staler
Memory health is a practice, not a one-time setup.
The rhythm matters as much as the architecture.
How We Help
Design, implement, and maintain AI memory systems
Memory Architecture
Design the right layer structure for your context. What belongs where, what loads when, how it all connects.
Implementation
Build the files, set up the retrieval, establish the workflows. From individual setups to team-scale systems.
Maintenance Protocols
Define the rhythms and processes that keep memory healthy. What gets updated when, how staleness is prevented.
Fix the Forgetting
AI memory is solvable. The right architecture, the right patterns, the right maintenance rhythm – and AI stops forgetting and starts compounding.
No commitment, no pitch. Just a conversation about where your AI memory stands and what healthier memory could look like.
Where To Go Next
Context Engineering
The broader discipline of designing what AI knows, when it knows it, and how that knowledge is structured.
The AI Literacy Gap
Why tool access isn't fluency – and what actually builds effective AI capability across organisations.
Agents & Orchestration
Beyond chat: how AI agents can handle complex, multi-step work with the right orchestration and memory.
AI Capability Overview
The full picture of how we help organisations build AI capability – context, skills, and orchestration.
Disclaimer: This content is for general educational and informational purposes only. Technical details reflect current understanding of LLM architecture and may evolve as the field develops. For specific guidance on AI memory implementation in your organisation, please consult appropriately qualified professionals.