I’ve tested enough “AI agents” now to know that most of them flop the minute you throw them a multi-step real-world problem. They will definitely parrot a quick solution if you feed them a single command — but as soon as you ask for real continuity or cross-session recall, everything comes crashing down.
In other words, no: HiiBo is not claiming to be an agent right now.
If that feels disappointing — good. We’re not here to sell you illusions of a half-baked “agent” that forgets your name after two lines of conversation, and can’t actually complete any valuable tasks. We’re not trying to deliver something that requires you to spend all your time fenagling it to work properly. That sucks.
Instead, we’re building a context management layer so that when we do roll out agent features, they won’t break under real usage. Here’s the why and how.
Agents That Don’t Track Context Are Dead on Arrival
“Let me handle all your tasks while you relax,” your agent chirps. It seems to think it’s Sonny (from iRobot).
But then, five minutes in, it forgets a crucial detail from step one and forces you to restate your entire request. Now it’s looking more like a dazed intern who misplaced the sticky note. The real culprit? Zero continuity.
Your agent is just a chatbot in an upscale costume, proclaiming “autonomous” capabilities without the memory to hold a conversation for more than two minutes.
A 2023 Forrester report found 58% of so-called “AI-driven agent solutions” flat-out failed on basic continuity tests — like referencing information provided earlier in the workflow. Why? They have a fancy interface and a few scripted workflows, but beyond that? No robust memory architecture. If the agent can’t recall your previous instruction or adapt to newly introduced variables, it’s not autonomous; it’s a glorified command prompt.
Moral of the Story: Without context management, these “agents” revert to ephemeral Q&A bots that repeat themselves or cause you to repeat yourself — neither of which spells progress.
Don’t be fooled by the snappy demos and marketing jargon: any agent worth its hype has to maintain your context across steps, sessions, and tasks. Period.
The HiiBo Difference
We’re building HiiBo Alpha as a cross-thread memory solution. Everything from your short convos to more complex tasks is contextually linked. We recognize that if your AI can’t remember you said “No onions” a minute ago, it certainly won’t be able to handle your five-step workflow.
HiiBo Alpha Is the Missing Foundation
When we call ourselves a “context management layer,” we mean it literally. HiiBo sits between you and your LLM(s). It intercepts ephemeral data that typically vanishes when you close a chat, organizes it, and re-injects only what’s relevant.
As a result, your interactions become fluid — with real memory behind them, not just a running transcript that eventually chokes on token limits.
Why Not Jump Straight to “Agent” Mode?
We’re Not Here to Pretend
Slapping “agent” on top of an LLM might fool someone for a second, shit it might even fool an investor. But the minute you need your AI to recall a detail from three conversations ago, you’ll see the cracks.Context First, Bells & Whistles Later
Agents revolve around consistent knowledge across tasks. No memory, no agent. We prefer to solve memory properly rather than feign a quick fix.
True Agents That Don’t Stutter
We do plan on building agent capabilities — once we’re confident in the memory base. A real AI agent should:
Orchestrate Tasks Across Apps: Email, Slack, calendar, CRM — bouncing around with full knowledge of your preferences.
Adapt to Different LLMs: Maybe GPT-4 for complex logic, Claude for cheaper text expansions, all while holding your “user context” in one place.
Chain Multi-Step Requests: If you say, “Book me flights, then find me a pet-friendly hotel that’s near the conference,” the agent shouldn’t have an identity crisis mid-conversation.
A true agent is free-flowing, cross-LLM, cross-application, and consistent over time. You can’t fake that with a quick script or ephemeral session data.
If you’ve been dazzled by “autonomous agent” demos, you might recall them glitching or freezing after the second or third step. They often pile a bit of automation on top of ephemeral memory. Kind of like building a skyscraper on quicksand.
It looks lovely until you ask for a mild cross-reference or mention one of your user preferences and cause the foundation to buckle.
The Stanford AI Index (2023) reported that while agent frameworks soared in popularity, “lack of robust memory systems” is the main reason most pilot programs collapsed once they scaled. This is not news to us.
The Product Ambassador Program — This Friday!
Yes, we’re focusing on memory first and not selling you any agentic illusions.
If you actually want to see how real multi-LLM continuity is built, come join our Product Ambassador program launching this Friday. Waitlist is live.
Early Access: See how we unify your ephemeral LLM calls into a single, persistent memory layer.
Influence Our Roadmap: We do plan to morph HiiBo into an agent eventually, but your feedback on the memory features will shape how fast (and how well) we get there.
Discounts & Perks: Because we appreciate early believers in building context before illusions.
Final Musings
Call me cynical, but I’ve come across a lot of “agents” that can’t do much beyond a single short action. Without real memory — i.e., stable, cross-task context — they’re just hype.
HiiBo might not be an agent (yet), but we’re laying the proper foundation: a context layer that supports real multi-step, multi-thread usage. That is what an agent truly needs to function without stuttering.
If you’re done with ephemeral “do-little” agents, come join us. Because when we do roll out agent features, you’d better believe they’ll have the continuity to back it up.
About the Author
Sam Hilsman is the CEO of CloudFruit® & HiiBo. If you want to invest in HiiBo or oneXerp, reach out. If you want to become a developer ambassador for HiiBo, visit https://HiiBo.app/dev-ambassadors