Every AI can generate. Few can understand.

What separates a chatbot that merely answers questions from an intelligent agent that truly solves problems? Context.

From Prompt Engineering to Context Engineering

When ChatGPT became popular, everyone started talking about prompt engineering — how to write smart instructions that guide AI models.

But as AI agents now run real work — handling customer tickets, checking invoices, predicting demand, and running workflows — prompts are no longer enough.

You can’t prompt your way into understanding a business.
You have to engineer its context.

Welcome to the next frontier of AI system design: Context Engineering — where success depends not just on what you tell the AI, but on the world of information you build around it.

What Is Context Engineering?

Context engineering means designing the informational environment an AI system needs to work intelligently.

It’s about helping the AI understand:

• Who it is (its role, goal, and access rights)
• Where it is (the system, domain, or company it belongs to)
• What it’s solving (the task, problem, or question)
• Why it matters (the outcome or business value)
• When to act (awareness of time and sequence)

If prompt engineering tells the AI what to do,
context engineering teaches it how to think and why it matters.

Think of it as moving from “writing instructions” to designing intelligence.

Why Context Matters in the Agentic Era

AI agents don’t just reply — they observe, reason, and act.
Each stage depends on the right context.

Stage	Context Needed	Example
Perception	What data it can see	Customer profile, logs
Reasoning	What it already knows	Manuals, rules, past tickets
Action	What it can use	APIs, workflows, approvals
Reflection	What worked before	Feedback loops, history
Awareness	What’s changing	New policies, current events

Without the right context, an AI agent is like someone walking into a meeting with no background or notes — reactive, but not really intelligent.

How to Engineer Context

In big organisations, data and knowledge are spread across many systems — CRMs, ERPs, ticketing tools, and emails.
Context engineering connects these dots and gives the AI a full picture to think clearly and act correctly.

Here’s how the best teams do it:

1. Start with truth: Link the AI to verified enterprise data using RAG (retrieval-augmented generation).
2. Show relationships: Build simple knowledge graphs that map people, processes, and systems.
3. Add memory: Keep short-term and long-term memory so the agent remembers what happened before.
4. Share meaning: Define what context different systems exchange — like APIs for knowledge.
5. Learn and reflect: Capture what worked, what didn’t, and adjust future responses.
6. Keep it safe: Make sure context follows company rules and privacy policies.

When you combine all these, your agents become aware, grounded, and consistent — not just reactive.

Real-World Example — Energy GPT

Let’s take an example from the Energy & Utilities world.

A “Customer Service GPT” for a utility company doesn’t just answer questions — it understands the full picture:

• Customer context: Tariff plan, meter type, usage pattern
• Operational context: Outage data, field crew availability, weather
• Organisational context: Billing rules, service levels, escalation paths
• Reflective context: Past conversations and feedback
• Regulatory context: GDPR and energy compliance requirements

Now, it can reason and respond intelligently:

“Based on your usage and today’s temperature, your high bill might be due to a faulty meter.
Would you like me to run a remote diagnostic?”

That’s context at work — invisible, powerful, and transformative.

The Future: Context as a Platform

Soon, enterprises will treat context as a platform layer, just like they manage code (DevOps) or models (MLOps).

You’ll see:

• ContextOps pipelines to refresh and validate knowledge
• Context SDKs to share data safely between systems
• Context graphs linking meaning across business units

Because in the age of intelligent agents:

Context becomes infrastructure.

You won’t just build models —
you’ll engineer meaning.

Final Thought

For years, we’ve mastered code engineering. Now it’s time to master context engineering —
because true intelligence doesn’t live inside the model,
it lives in the context that shapes how the model thinks.