₦airaland Forum

If you are a follower of the AI revolution or are just interested in AI news, one of the biggest hypes are AI Agents. AI Agents are beyond simple LLM wrappers like your everyday AI powered app. Agents are sophisticated AI enabled systems that use LLMs for reasoning, planning and acting. There are different types, built for different tasks.


When it comes to Physical AI (AI that acts in the real world, not only browser or terminal based), LLMs and VLMs are still struggling, hence the low implementation or adoption of AI agents in that domain. To act successfully in the real world, an agent would require spatial intelligence, memory and some understanding of the physical world. These are all areas in which present day AI lacks. See here for yourself;

Use the following prompt in any AI model so you can make the observation for yourself;

PROMPT: You are a vision-language model analyzing your own outputs.
Identify:
1. Objects you cannot track across images
2. Relationships you cannot remember
3. Facts you recompute instead of store
Explain how persistent object memory would improve your performance.

The honest answer will shock you. Meanwhile Some Chinese research labs are coming out with augmentation solutions to solve the limitations you just observed with the AI model. A Nigerian startup https://coreworldmodel.com is also at the forefront of providing this solution through its plug-and-play lightweight stateful perception middleware.

You can read more about how startups are helping AI Agents navigate the real world here https://techpoint.africa/partner-pages/core-bringing-ai-agents-from-the-browser-to-the-physical-world/

9jageek:
If you are a follower of the AI revolution or are just interested in AI news, one of the biggest hypes are AI Agents. AI Agents are beyond simple LLM wrappers like your everyday AI powered app. Agents are sophisticated AI enabled systems that use LLMs for reasoning, planning and acting. There are different types, built for different tasks.


When it comes to Physical AI (AI that acts in the real world, not only browser or terminal based), LLMs and VLMs are still struggling, hence the low implementation or adoption of AI agents in that domain. To act successfully in the real world, an agent would require spatial intelligence, memory and some understanding of the physical world. These are all areas in which present day AI lacks. See here for yourself;

Use the following prompt in any AI model so you can make the observation for yourself;

PROMPT: You are a vision-language model analyzing your own outputs.
Identify:
1. Objects you cannot track across images
2. Relationships you cannot remember
3. Facts you recompute instead of store
Explain how persistent object memory would improve your performance.

The honest answer will shock you. Meanwhile Some Chinese research labs are coming out with augmentation solutions to solve the limitations you just observed with the AI model. A Nigerian startup https://coreworldmodel.com is also at the forefront of providing this solution through its plug-and-play lightweight stateful perception middleware.

You can read more about how startups are helping AI Agents navigate the real world here https://techpoint.africa/partner-pages/core-bringing-ai-agents-from-the-browser-to-the-physical-world/

Agents can't take hardware engineering jobs for now . Those guys are richer than software guys . Millions of dev globally are switching.

Tried that prompt on Claude — the self-awareness about its own memory limitations was surprisingly honest. It basically admitted it can't track objects across images or retain relational context between frames. That's the core gap right there.

The spatial intelligence problem is harder than it looks. Current models see images as static, independent snapshots with no continuity between them. That's why robotics demos still fail in uncontrolled environments — the model can't tell that the cup it saw 2 seconds ago is the same cup now rotated slightly. Humans do this effortlessly with almost zero compute.

The plug-and-play middleware angle is clever as a short-term solution. Rather than retraining the base model, you bolt on stateful perception as a layer. Curious whether the Nigerian startup is targeting specific verticals (logistics, manufacturing) or going general-purpose. General-purpose physical AI is still a long way off, but vertical-specific solutions can work today with constrained environments.

The Chinese research labs racing on this makes sense — they're heavily invested in robotics manufacturing. Whoever solves persistent object memory and scene continuity properly owns the physical AI space.

as69:
Tried that prompt on Claude — the self-awareness about its own memory limitations was surprisingly honest. It basically admitted it can't track objects across images or retain relational context between frames. That's the core gap right there.

The spatial intelligence problem is harder than it looks. Current models see images as static, independent snapshots with no continuity between them. That's why robotics demos still fail in uncontrolled environments — the model can't tell that the cup it saw 2 seconds ago is the same cup now rotated slightly. Humans do this effortlessly with almost zero compute.

The plug-and-play middleware angle is clever as a short-term solution. Rather than retraining the base model, you bolt on stateful perception as a layer. Curious whether the Nigerian startup is targeting specific verticals (logistics, manufacturing) or going general-purpose. General-purpose physical AI is still a long way off, but vertical-specific solutions can work today with constrained environments.

The Chinese research labs racing on this makes sense — they're heavily invested in robotics manufacturing. Whoever solves persistent object memory and scene continuity properly owns the physical AI space.

TerraHaptix is our own Anduril , Nigerians young tech engineers are breaking the barriers and building solutions . The rest are too afraid to start . Now they have investors all around the world even Nigerian Governments offer them recognition.

I specifically like this quote from your response " Feels like we’re moving from “AI as a tool” to “AI as an active participant” in systems."

AI is essentially an active participant, it helps us figure things out. In our new whitepaper on a new marketing phenomenon we named Prompt Marketing, your AI sort of helps convince you about what augmentation or what other product add-ons it needs to properly do its job. It is an active participant because it sort of gives you an analytic assessment of the problem and its capabilities, and how the product that is prompt marketed could help it gain.

The product developer or seller will need to find the right prompt to design that will bring this honest answer from the AI, then all they need to do is convince the prospect to use the prompt, and the AI will do the rest. Sort of acting like a sales representative.

Download and copy the strategies from the Prompt Marketing Playbook https://boltzmind.ai/assets/promptmarketing_whitepaper.pdf

andrew1703:
This is actually where things start getting really interesting. AI agents moving beyond the browser into real-world environments changes the entire scope of what these systems can do. It’s no longer just about generating responses — it’s about interacting with systems, devices, and workflows in a more practical way.

A lot of development is also happening behind the scenes in terms of how these agents are built and managed. I was exploring this area recently and came across something here:
https://www.algorithmshift.ai/

It gives a glimpse into how AI-driven workflows and development environments are evolving, especially as agents become more capable. Feels like we’re moving from “AI as a tool” to “AI as an active participant” in systems.