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Welcome Back to XcessAI
Hello AI explorers,
Earlier this year, we explored Agentic AI — how autonomous AI agents are emerging as digital co-workers capable of handling complex tasks.
But as businesses start experimenting with AI agents, many are running into a frustrating reality: these agents still fail more often than they succeed.
Not because the technology isn’t there — but because most companies are missing a critical piece of the puzzle.
It’s called Context Engineering.
Today, we’ll explore what that means, why “few-shotting” is killing your AI’s performance, and how smart executives can design environments where AI agents actually deliver.
The Problem: Under-Briefed AI Agents
Imagine you hire a new employee, tell them:
“Please handle our client onboarding.”
… and walk away.
What happens?
They’ll fumble, ask basic questions, and make mistakes.
That’s exactly what happens when you deploy an AI agent with vague prompts or incomplete information.
In AI terms, this is called few-shot prompting — giving the AI too little context, expecting it to generalize, and being disappointed when it hallucinates or misfires.
Agents aren’t failing because they’re dumb. They’re failing because we’re briefing them like interns instead of preparing them like professionals.
Context Engineering — The Missing Ingredient
Context Engineering is the practice of designing the information environment around your AI agents to ensure they perform tasks reliably and accurately.
Think of it as:
Giving your AI agent a full dossier before the meeting.
Setting up the right tools, documents, and workflows so it knows where to look.
Structuring the prompts, data access, and task flows so it doesn’t need to “guess.”
In the words of Manus (the AI company that coined the term):
“AI agents don’t generalize well from just a few examples. They need a structured, engineered context to operate effectively.”
Don’t Get Few-Shotted — Why Context Matters More Than You Think
“Few-shotting” is a term borrowed from AI training methods. It refers to giving an AI just a few examples (or minimal context) and expecting it to generalize correctly. This can work for simple tasks — but for complex, nuanced workflows, it often falls short.
In business terms, it’s like giving an intern three examples and expecting them to draft a Board-level report.
Manus, a company specializing in AI agent design who recently published a framework on context engineering, highlighted this problem. In their words, failing to provide structured context leads to fragile, unreliable AI behavior.
When you assign an AI agent a task with minimal guidance, it will try to predict what you probably want — but that guesswork often results in:
⚠️ Hallucinations — wrong answers presented confidently
⚠️ Relevance Drift — going off-topic or missing key details
⚠️ Inconsistent Outputs — varying results for similar tasks
⚠️ Fragile Automation — agents that fail when variables change slightly
In critical business functions — like customer service, market research, or compliance — these failures aren’t quirky. They’re costly.
Real-World Scenarios Where Context Engineering Wins
Customer Support Automation
Instead of a chatbot guessing answers from generic FAQs, context-engineered agents are given access to live product manuals, customer history, and escalation rules.Market Research Agents
Rather than asking an AI “Summarize competitor X,” the agent is prompted with pre-structured data, including competitor filings, product releases, and sentiment analysis.HR Onboarding Bots
An AI agent that doesn’t just send templates but adapts workflows based on department, role, and region-specific compliance requirements.
In all these cases, the difference isn’t the AI model — it’s the context engineering.
How to Build Context-Aware AI Systems
For business leaders, you don’t need to dive into model training. But you do need to ensure your AI initiatives are designed with these principles in mind:
🗂️ Structured Knowledge Bases
Ensure your agents can retrieve accurate, up-to-date information from a central source.
🧩 Prompt Chaining & Templates
Design multi-step prompts that guide the AI through tasks systematically, reducing ambiguity.
🧑💼 Human-in-the-Loop Design
Involve human oversight for high-stakes decisions — AI proposes, humans approve.
🔍 Continuous Context Feedback
Set up systems where agents learn from corrections and refine their task execution.
Business Takeaway — Engineer the Environment, Not Just the Agent
Buying the best AI agent is like hiring a top-tier employee.
But if you throw them into a messy office, give vague instructions, and no resources — they’ll fail.
Your AI agents need engineered environments where they have clear briefs, reliable information, and structured workflows.
The businesses that succeed with Agentic AI won’t be the ones with the biggest models — they’ll be the ones that design the smartest context.
Final Thoughts
The age of Agentic AI isn’t just about building smarter bots. It’s about designing systems that set them up for success.
When an AI agent misfires, the problem isn’t always the model. It’s often the missing context. Ask yourself — did I engineer the right context?
Until next time,
Stay structured. Stay strategic.
And keep exploring the frontier of AI.
Fabio Lopes
XcessAI
P.S.: Sharing is caring - pass this knowledge on to a friend or colleague. Let’s build a community of AI aficionados at www.xcessai.com.
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