Context Is Everything:
Why AI Keeps Failing You (And What To Do About It)
Mark my words: Context is everything.
You've felt this, right? AI is brilliant at firing off a quick email response or summarizing a meeting. Sharp, fast, helpful. Then you try something deeper—analyzing your Q3 strategy, drafting that complex proposal, working through a business model—and it suddenly becomes useless. Gives you generic nonsense. Forgets what you said three prompts ago. Contradicts itself.
You probably blamed the AI. I did too.
Here's what I learned: it's not a limit of AI. It's how we use AI.
And in 2025, while everyone's obsessing over which model is best or crafting the perfect prompt, the real winners have figured out something completely different: context management is the game.
The Prompt Engineering Dead End
For months, I was convinced prompt engineering was the answer. Get the prompt right, get the output right. I read the guides. I tried the frameworks. I tortured my prompts into these elaborate instructions with examples, constraints, and formatting rules.
Sometimes it worked. Mostly it didn't.
Then I noticed something that changed how I think about AI entirely: the LLM giants—OpenAI, Anthropic, Google—they're all moving in the same direction. Not better prompts. Two things: agentic workflows and context management.
Anthropic just published their complete guide on context engineering. Microsoft's entire Build 2025 conference centered on multi-agent orchestration. Even Shopify's CEO tweeted that context engineering is replacing prompt engineering as the critical skill.
That's when it clicked for me. The game isn't about asking better. It's about giving AI what it needs to think with you, not just respond to you.
What Context Actually Means (And Why It Breaks)
Here's the technical reality that nobody explains clearly: AI agents have massive context windows—170,000 tokens for Claude, similar for GPT-4. That's enough for hundreds of pages of information.
But here's what the research shows: once you fill more than 40% of that space, performance falls off a cliff.
Anthropic's own studies on "context rot" prove this. As the number of tokens in the context window increases, the model's ability to accurately recall information decreases. It's not just about hitting the limit—it's about cognitive load. LLMs, like humans, lose focus when they're overwhelmed with information.
I was burning through context without realizing it. Every time I said "no, try again" or "that's not what I meant," I was filling up the tank. Three rounds of back-and-forth, and the AI was already drowning in noise.
Here's the kicker that nobody talks about: when you add a file to project memory in GPT, Claude, or Grok, that entire file gets called with every single message you exchange with the AI. Add 2-3 reference documents, exchange 10 messages, and you've blown past the 40% threshold. The AI is now working with degraded attention across everything.
When you hit that wall, you stop. Extract what matters. Start fresh. Every single time.
That single shift cut my frustration in half.
The Hierarchy of AI Failure
After months of trial and error (lots of error), I've noticed a pattern in how AI projects go sideways. It's not random. There's a hierarchy:
A vague prompt creates bad output.
This is what most people worry about. "Did I phrase that right?" But honestly, this is the easiest failure to fix. You iterate, you refine, you get better output.
A bad brief creates ten bad deliverables.
This is where things get expensive. When you give AI a fuzzy objective—"help me with our marketing strategy"—it generates multiple variations of mediocre work. You spend hours reviewing garbage because you never defined what good looks like.
A misunderstood context creates an entire project going sideways.
This is the killer. When the AI doesn't have the right information about your business, your constraints, your goals, it produces work that's technically correct but strategically useless. And you don't realize it until you're three weeks in and have to start over.
Fix problems upstream, not downstream.
What the Industry Figured Out (That Nobody's Talking About)
While most companies are still trying to get ChatGPT to write better emails, the leaders are rebuilding how they work entirely.
According to Gartner's projections, by 2028, 33% of enterprise software will depend on agentic AI. But right now, 85% of agent implementations fail. The difference? Context architecture.
Here's what actually works in production:
- Just-In-Time Context Retrieval
The smartest systems don't pre-load everything. They fetch information dynamically when it's needed. Claude Code, Anthropic's coding tool, uses this approach: instead of stuffing your entire codebase into context, it navigates files like a developer would, reading only what's relevant to the current task.
This mirrors how humans work. You don't memorize your company's entire handbook. You look up the relevant policy when you need it.
- Progressive Disclosure
Agents can discover context incrementally through exploration. File sizes suggest complexity. Naming conventions hint at purpose. Timestamps indicate relevance. The AI builds understanding layer by layer, maintaining only what's necessary in working memory.
Microsoft's research on this is fascinating—their multi-agent workflows achieved 84% reduction in token consumption while maintaining coherence across 100-turn dialogues. That's not a marginal improvement. That's a different approach entirely.
- KV-Cache Optimization
This is getting technical, but it matters: production AI systems rely heavily on KV-cache hit rates to manage cost and latency. Companies building production agents—like Manus, which we'll get to—report that keeping your prompt prefix stable is critical. Even a single-token difference can invalidate the cache and 10x your costs.
For enterprise applications, this isn't optional. When you're running thousands of agent interactions daily, context management becomes the difference between viable economics and burning money.
The Personal Wake-Up Call
Let me tell you about the moment this all became real for me.
I was working on a personal coding project—something complex I wanted to build. I asked an AI agent to help, and it generated thousands of lines of code. Beautiful, sophisticated, exactly what I asked for.
Completely unreviewable.
I stared at it for hours. I couldn't tell if it was good. I couldn't spot the bugs. I couldn't understand the architecture choices. The cognitive load was overwhelming.
So I stopped trying to review the implementation.
Instead, I went back and wrote a detailed spec first. Clear requirements. Test cases. Expected behavior. Then I had the AI generate code against that spec.
The difference was night and day. The code was cleaner. The errors were obvious. The whole process was faster.
That's when I realized: I was reviewing compiled output when I should have been reviewing the blueprint. Same principle applies whether you're working with code, strategy documents, or complex analyses. AI can handle execution if you nail the architecture.
This isn't just about coding. DigitalOcean's documentation on context management shows the same pattern across all domains: insufficient context causes hallucinations (AI inventing things that don't exist), while context overflow causes unfocused, generic responses.
The solution? Comprehensive requirements divided into focused tasks, executed one at a time with fresh context. Different tasks need different types of context. Each piece of work needs its own tailored information set.
Keep your working context tailored to the specific task at hand.
What This Means For You (Regardless of Your Function)
You don't need to become a prompt engineer. You need to become a context architect.
Here's what that actually means in practice:
Before asking AI to help with something complex:
Map what information it actually needs—not everything, just what matters. If you're analyzing competitive positioning, the AI doesn't need your entire company history. It needs: your current offerings, your competitors' key differentiators, your target customers, and your constraints. That's it.
Give it clear boundaries and structure upfront. "Here's the decision framework we use. Here are the three metrics that matter. Here's what good looks like." Not after it generates something wrong—before.
Build the problem step by step, not all at once. Break "develop our Q4 strategy" into: first, analyze market trends. Then, assess our positioning. Then, identify opportunities. Then, evaluate resource requirements. Each step gets fresh context focused on that specific question.
When it starts drifting, extract the good parts and restart fresh. Don't keep arguing with AI in the same conversation thread. That's like trying to have a focused discussion in a room where everyone's shouting. Pull out what's useful, open a new conversation with clean context, and continue there.
In Practice:
I've shifted how I work with AI entirely.
I'm not writing code anymore in my personal projects. I'm designing the scaffolding for AI to work within.
I'm not debugging outputs. I'm debugging my inputs.
I'm not spending hours iterating on prompts. I'm spending minutes architecting context.
And the results are dramatic. I'm building things faster than when I coded everything myself. The output is more consistent, better tested, more maintainable.
But it required letting go of habits I built over 20+ years: being the person who writes and reviews every line.
That was harder than I expected.
What Industry Leaders Are Actually Doing
The companies winning with AI in 2025 aren't the ones with the best prompts. They're the ones who figured out context management first.
BCG reports that early adopters of agentic AI see 20-30% faster workflow cycles and significant reductions in back-office costs. But the key finding: "AI agents are not just improving workflows; they're redefining how businesses operate."
ServiceNow's AI agents are reducing manual workloads by up to 60% through effective context management in IT, HR, and operational processes.
McKinsey describes the emerging "agentic organization"—where work is orchestrated through networks of AI agents rather than traditional hierarchies. They predict that by 2027, AI systems could complete four days of work without supervision.
But here's what's critical: all of these implementations succeed or fail based on context architecture.
Synchrony Financial's SVP of Innovation puts it this way: "Responsible AI is core to what we do." They're not moving fast and breaking things. They're architecting context carefully within highly regulated constraints.
Stanford Health Care is using Microsoft's healthcare agent orchestrator to build AI agents that reduce administrative burden for tumor board preparation. The agents work because they have carefully curated, domain-specific context.
The Uncomfortable Truth
I've been watching this shift happen across the industry, and there's a pattern emerging.
The people succeeding with AI aren't necessarily the most experienced professionals. They're the ones who figured out context management first.
I've talked to leaders who have junior team members outperforming senior staff when using AI. Not because the juniors are smarter. Because they approach AI differently. They treat it like a tool that needs clear architecture, not a colleague who should "just understand."
They provide context. They hit limits and reset. They don't argue with hallucinations—they recognize context drift and start fresh.
Meanwhile, experienced professionals often spend hours reviewing AI outputs line by line, applying expertise at the wrong level. It's like checking the assembly code when you should be reviewing the system design.
The spec is becoming the product. The implementation is becoming compilation output.
This requires a different way of thinking about expertise.
Where This Goes
The skill isn't writing better prompts. It's not even using AI better. It's architecting the problem so AI can actually solve it.
This is a fundamentally different muscle than what got most of us here.
In 2025, according to the UiPath Agentic AI Report, 93% of IT executives are extremely interested in agentic workflows, and 37% are already using them. The adoption curve is steep.
But adoption without understanding context architecture leads to that 85% failure rate.
The teams and leaders winning with AI aren't the ones with the most sophisticated models. They're the ones who figured out that context management is the foundation everything else is built on.
It's not just clever prompting. It's software architecture for intelligence.
I don't know if this applies to every function or every role. But I do know that every leader I talk to hits the same wall: AI works great until it doesn't, and they don't know why.
Now you know why.
Context management.
The question isn't whether AI will transform how your function works. It's whether you'll be the one designing that transformation or trying to optimize the old way of working with new tools.
What's your experience? Where does AI break down for you?
Drop me a message—genuinely curious what you're seeing in your world.
Further Reading:
- Anthropic's Guide to Context Engineering
- Microsoft Build 2025: The Age of AI Agents
- McKinsey: The Agentic Organization
Ivan Paudice leads digital innovation at GE Aerospace and teaches venture design at University of Naples. After 20+ years building everything from wind turbines to digital platforms across 9 countries, he's now figuring out how AI changes the game. Still learning. Connect on LinkedIn.
