I went to Vistage Chairworld 2026 in San Diego last week with my buddy Steve. Steve's my secret weapon. He studies attendees before events, preps notecards on the people I need to meet, and whispers the right intel at the right moments.
Steve is excellent at his job.
I, on the other hand, am the problem.
Let me explain what I mean. AI context failures are real, and this conference and his work are the perfect example to explain these nine scenarios.
Because these nine scenarios? They're the exact same failures I keep seeing in AI agents.
The Problem Isn't Your Data Pipeline
Here's what I keep reading about. A team builds an AI agent. They invest weeks in the retrieval layer, making sure the agent has access to the right documents, the right CRM data, the right knowledge base. They get the context pipeline working beautifully.
Then the agent produces garbage.
So they blame the data. They add more documents. They tweak the retrieval. They adjust the chunking strategy. They rebuild the vector database.
None of it helps, because the data was never the problem.
What I've learned from building and debugging these systems is that most ai context failures happen after retrieval. The agent has the right information. It just does the wrong thing with it. And until you can name the specific way it's going wrong, you're stuck fixing the wrong layer.
There are nine of them. And Steve, my conference wingman, is the perfect way to see all nine.
Context Rot
Imagine Steve pulls me aside at 9am and says, “The woman in the blue jacket is Karen. She chairs a group of 16 CEOs in the Pacific Northwest and she's been looking for better assessment tools for her members.” Clear. Specific. Actionable.
Now imagine I bump into Karen four hours later at the coffee bar. By then I've had a dozen conversations, sat through two breakout sessions, and eaten lunch. I blank. “Hey… you're a chair in… Oregon?”
Steve told me exactly what I needed. I just let it slip away over the course of a long, noisy day.
AI agents do this constantly. Early in a conversation or session, the model receives critical context. But as the interaction grows longer and more context accumulates, that early signal degrades. Not because it was removed, because it gets buried under volume. The agent still “has” the information. It just can't surface it when it matters.
If your agent performs well in short interactions but falls apart in longer sessions, you're probably looking at context rot.
Context Poisoning
Think of it this way. Steve's notecard on Marcus says, “tenured chair, 20-year track record, incredibly well-respected in his peer group.” But imagine some random guy at the lunch table tells me, “Oh Marcus? Yeah, he's lost a few members recently. Group's struggling.”
If I choose to believe the lunch gossip over Steve's vetted research, I'm now treating Marcus like he's on the decline based on intel from someone I've never met who had his own ax to grind.
This is what happens when agents treat all input as equally authoritative. A passing mention in an email thread gets the same weight as a formal policy document. User speculation gets treated like verified fact. The agent doesn't distinguish between carefully sourced information and noise, so low-quality input poisons high-quality context.
If your agent confidently presents information that contradicts your best data sources, you've got context poisoning.
Context Confusion
Here's another one. Imagine Steve tells me clearly: “David just expanded to a second group focused on key executives. Jason has been chairing one tight group of 12 for fifteen years and that's how he likes it.”
Two chairs. Two distinct stories. Now imagine I walk up to Jason and say, “So I hear you're scaling to a second group, congrats!” Jason looks at me sideways.
Steve's intel was perfect. I just wired it to the wrong face.
AI agents do this with entities. They have clean information about Company A and clean information about Company B, but when generating a response, they attribute Company A's characteristics to Company B. This gets worse with entities that share surface-level similarities, like two companies in the same industry, or two contacts with overlapping roles.
If your agent produces responses where the facts are individually correct but assigned to the wrong entities, that's context confusion.
Context Dilution
Imagine Steve whispers one thing before my conversation with a veteran chair who manages four groups: “She's been telling people she needs a better way to assess her members' motivations during one-to-ones.”
That's gold. But instead of leading with that, imagine I start spiraling through everything else I've picked up. Her LinkedIn post about a leadership retreat. The breakout she attended on group dynamics. Something I overheard about her switching scheduling platforms. I bury Steve's sharp, actionable signal under a pile of tangential observations.
This is one of the most common ai context failures in retrieval-augmented systems. The agent retrieves the right information alongside a bunch of adjacent-but-irrelevant information. The critical signal gets diluted by volume. The response addresses everything superficially instead of the most important thing deeply.
If your agent's responses feel broad and unfocused even though the right information is in the context window, you're dealing with context dilution.
Context Leakage
Think about this scenario. Steve quietly mentions, “By the way, Rachel told me she's thinking about stepping down from one of her groups. Keep that to yourself.” Now imagine ten minutes later, I'm in a hallway conversation with another chair from Rachel's region and I casually say, “Yeah, sounds like Rachel might be making some changes to her groups.”
Steve kept the boundary. I didn't. Private context leaked because I wasn't careful about which conversation I was in.
This is a serious one for multi-user and multi-session agent systems. Information from one user's context appears in another user's response. Details from a private conversation surface in a shared workspace. The agent doesn't maintain boundaries between contexts that should be isolated.
If your agent is surfacing information in contexts where it shouldn't appear, that's context leakage, and it's not just a quality problem. It's a trust problem.
Context Hallucination
Imagine I'm walking toward a woman at the reception. Steve hasn't said a word. He doesn't have a card on her. But I feel like I should know her, so I convince myself we met at a Vistage event in Dallas two years ago. I walk up with, “Great to see you again!”
She's never been to a Dallas event. Steve would've told me if there was a connection. There wasn't. I invented one because silence felt uncomfortable.
This is the most widely discussed context failure, and for good reason. The agent generates confident, specific information that doesn't exist in any source. It fills gaps in its context with plausible-sounding fabrications. What makes this especially dangerous is that the fabrications are often detailed and convincing, precisely because the model is optimizing for coherence rather than accuracy.
If your agent is producing confident, specific claims that aren't grounded in any source material, that's hallucination.
Context Staleness
Here's how this one works. Imagine Steve has two cards on a particular chair. The old card says, “chairs a CEO group in Denver.” The updated one says, “moved to Austin in October, transitioned her group and is now building a new one from scratch.”
Steve hands me the new card. I glance at it, but the old one is already stuck in my head. So I walk up and ask how things are going with the Denver group.
Steve did his job. I just grabbed the stale version because it was more familiar.
Agents hit this when they have access to both current and outdated information and use the wrong one. Maybe the knowledge base was updated but the cached embeddings weren't. Maybe the agent retrieves an older document that ranked higher in similarity search than the newer one. The information is technically available, but the agent defaults to the version that matched first, not the version that's correct.
If your agent uses outdated information when a current version exists in the same system, that's context staleness.
Context Anchoring
This one's subtler. Imagine Steve's first card on Ryan says, “newer chair, still finding his footing.” Fair enough, that was true a year ago. But throughout the day, Steve keeps handing me revised cards: “Ryan's group just hit highest retention in his region.” “Ryan spoke on the main stage about member engagement.” “Ryan's been mentoring two new chairs.”
Imagine I keep nodding, putting the new cards in my pocket, and still thinking of Ryan as a rookie.
Steve isn't the one who's stuck. I am.
This is different from staleness. The agent has received updated context, but it over-weights the initial framing and under-weights subsequent updates. The first piece of information it received about an entity acts as an anchor, and later corrections or additions don't shift the output proportionally. You see this in agents that form an early “impression” of a topic and then resist updating even when new information contradicts it.
If your agent keeps producing outputs that reflect its earliest context about a topic while ignoring recent updates, that's context anchoring.
Context Fragmentation
This might be the most costly failure of the nine. Imagine Steve tells me three things at different points in the day. Morning: “That guy chairs four groups, all mid-market CEOs.” After lunch: “He just posted on the Vistage community board about wanting motivation-based tools for his one-to-ones.” Before the reception: “You told me chairs with multiple groups are your ideal buyers this quarter.”
Steve delivered all three pieces clearly, at the right moments. But imagine I never connect them. When the guy walks up to me, all I manage is, “So… how many groups are you running?”
Steve built the puzzle. I never bothered to look at the picture on the box.
The agent has all the relevant pieces of context distributed across its input, but it fails to synthesize them into a coherent picture. Each piece is accurate on its own. The agent can retrieve any individual fact. But it never connects them into the insight that would actually be useful.
If your agent has access to multiple relevant facts but never combines them into a unified response, that's context fragmentation.
The Pattern Behind All Nine
In every one of these scenarios, Steve did everything right. He researched. He briefed. He updated. He whispered at the right moments. He kept boundaries.
Every failure was mine. I forgot. I got distracted. I trusted the wrong source. I crossed wires. I made things up. I couldn't let go of a first impression. And I never synthesized what was right in front of me.
That's the difference between having great context and actually using great context. And it's the exact problem every AI agent faces. The retrieval system can be perfect, but the reasoning layer still has to do its job.
What to Do With This Framework
Here's why naming these matters. When an AI agent produces a bad output, the instinct is to blame the data. Add more documents. Improve the retrieval. Rebuild the knowledge base.
But if the failure is context rot, more data makes it worse, not better. If the failure is context anchoring, reingesting the same updated information won't help because the agent already has it. If the failure is fragmentation, the data is already there. The agent just can't connect it.
The diagnostic question isn't “does the agent have the right information?” It's “which of these nine failures is breaking the reasoning?”
Once you can name the failure, you can target the fix. Context rot needs attention management. Poisoning needs source weighting. Confusion needs entity disambiguation. Dilution needs relevance filtering. Leakage needs boundary enforcement. Hallucination needs grounding checks. Staleness needs recency ranking. Anchoring needs update weighting. Fragmentation needs synthesis prompting.
Nine different failures. Nine different fixes. None of them are “add more data.”
Steve's Still Ready
Steve will still be at the next conference. Notecards in hand. Intel sharp. Boundaries clean.
The question was never whether Steve did his job. The question is whether you'll do yours.
If you're building AI agents and the outputs keep disappointing you, stop blaming Steve. Start asking which of these nine ai context failures is actually breaking your system.
Because the data isn't the problem. What you're doing with it is.