AI CAPABILITY • PRACTICE
Context Engineering
The right information, at the right time
In 30 Seconds
Most AI failures aren't model problems. They're context problems. Context engineering is the discipline of designing what AI knows, when it knows it, and how that knowledge is structured.
This is where strategy becomes capability. Without the right context architecture, even the best AI strategy remains a document on a shelf.
Our core expertise: This is Practice tier work— designing and implementing the context systems that make AI useful in your specific environment. Not one-off prompts, but persistent capability.
The Shift from Prompts to Systems
Prompt engineering asks: “How do I phrase this question?”
Context engineering asks: “What does AI need to know to answer well?”
Anthropic defines it as “designing dynamic systems that provide AI models with the right information at the right time.” It's the evolution from crafting individual queries to architecting information environments.
| Prompt Engineering | Context Engineering | |
|---|---|---|
| Focus | The question | The knowledge |
| Scope | Single query | Entire system |
| Approach | Craft better prompts | Design information flow |
| Result | Better answers | Consistent capability |
The Context Problem
More context doesn't mean better performance.
Research shows input length alone can reduce AI accuracy by 14-85% – even when all information is relevant.
Lost in the Middle
Models favour information at the start and end of context, missing what's in between.
Context Rot
Quality degrades gradually as context accumulates and ages without maintenance.
Signal Dilution
Important information drowns in noise when everything is loaded indiscriminately.
Most teams dump everything into context. That's like answering every question by reading the encyclopaedia aloud.
Four Strategies for Managing Context
Based on Anthropic's framework for effective AI systems
1. Write
Persist externally
Store information outside the context window for later retrieval. Files, databases, knowledge bases – anything that persists beyond the session.
2. Select
Load only what's relevant
Retrieve context based on the task at hand, not everything available. Dynamic retrieval, semantic search, just-in-time loading.
3. Compress
Summarise, don't accumulate
Keep context lean through intelligent summarisation. Archive old content, preserve decisions, trim the unnecessary.
4. Isolate
Separate contexts for separate concerns
Don't let different workstreams pollute each other. Multi-agent architectures, session boundaries, role-specific loading.
Layered Context Architecture
Effective AI systems organise context in layers – from stable foundations to ephemeral working memory. Higher layers change rarely; lower layers are session-specific.
The art is knowing what belongs where.
Who Benefits from Context Engineering?
Individuals
- • Consistent AI results across sessions
- • Build on previous work, not from scratch
- • Reduce time re-explaining context
Teams
- • Reduce hallucinations through better knowledge
- • Enable handoffs between human and AI
- • Shared context across team members
Organisations
- • Multi-agent coordination without pollution
- • Governance and compliance controls
- • Scalable knowledge management
Context Engineering in Practice
We design and implement context systems for organisations building serious AI capability. From individual productivity to enterprise-scale agent orchestration.