Context Is the Product
Vishal Sachar
Co-Founder & CEO of CLRT
The quality of an AI agent is mostly decided before it runs a single step, by a choice almost nobody talks about: what you put in front of it. An agent needs six kinds of context, and knowing what goes where is the craft that separates a reliable system from an expensive guess.
The six are straightforward once named. Instructions: its role, goals, and boundaries. Knowledge: the documents and domain facts it works from. Memory: what happened before, the persistent state. Examples: a few demonstrations of what good looks like. Tools: the systems it can act through. Guardrails: the hard limits it must never cross.
The real decision is not which of these to have. It is where each one lives. Some context must be loaded every single time, always present, because the agent can never be allowed to forget it. Its core instructions and its safety rules belong here. This is reliable and it is expensive, because everything you keep permanently in front of the model is paid for on every interaction.
Other context should load only when it is needed. The detailed knowledge for a specific task, the result of a particular tool call, the documents relevant to this one question. Loaded on demand, used, and released. This is efficient and it scales, because you pay only for what the task actually requires.
Get this division wrong in either direction and you feel it. Put everything in the always-loaded layer and costs balloon while the model drowns in detail it does not need. Put the wrong things on demand and the agent forgets something critical at the worst moment. The best systems treat this as a real architectural decision, written down, reviewed, and versioned like code, not left to chance.
Context is not the setup for the product. In an agentic system, context is the product. The model is just the part that reads it.
A deeper dive
The division between always-loaded and load-on-demand is, underneath, an economic decision, and it is measurable. Everything in the always-loaded layer is paid for on every single turn, so it should hold only what is small, stable, and non-negotiable: the role, the core rules, the hard guardrails. Everything else belongs in the dynamic layer, pulled in only when the task calls for it. The standard mechanism for dynamic knowledge is retrieval, where the system fetches just the relevant documents at the moment they are needed rather than carrying the whole library in memory. Skills work the same way, triggered by task match. Tool results arrive only after a tool runs. The reason mature teams version their context like code is that this allocation drifts: a prompt that started lean accumulates "just in case" additions until every interaction is carrying a paragraph it almost never needs. Treating context as an architecture you review, rather than a setting you forget, is what keeps an agent both affordable and reliable as it grows.
Key terms
- Instructions
- Its role, goals, and boundaries.
- Knowledge
- The documents and domain facts it works from.
- Memory
- The persistent state of what happened before.
- Examples
- A few demonstrations of what good looks like.
- Tools
- The systems it can act through.
- Guardrails
- The hard limits it must never cross.
Work with CLRT
Running AI on more than guesswork means designing what it sees. CLRT builds the context architecture behind agents that stay reliable and affordable as they scale. Let us look at yours.
Vishal Sachar
Vishal Sachar is the Co-Founder and CEO of CLRT, where he helps UAE businesses make sense of applied agentic AI and put it to work. He writes on agentic systems, AI governance, and the economics of automation. Reach him at vishal@clrtstudio.com or on LinkedIn.
