{"id":77611,"date":"2026-04-16T02:08:14","date_gmt":"2026-04-16T02:08:14","guid":{"rendered":"https:\/\/diyhaven858.wasmer.app\/index.php\/ais-next-bottleneck-isnt-the-models-its-whether-agents-can-think-together\/"},"modified":"2026-04-16T02:08:14","modified_gmt":"2026-04-16T02:08:14","slug":"ais-next-bottleneck-isnt-the-models-its-whether-agents-can-think-together","status":"publish","type":"post","link":"https:\/\/diyhaven858.wasmer.app\/index.php\/ais-next-bottleneck-isnt-the-models-its-whether-agents-can-think-together\/","title":{"rendered":"AI's next bottleneck isn't the models \u2014 it's whether agents can think together"},"content":{"rendered":"


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AI agents can connect together, but they cannot think together. That\u2019s a huge difference and a bottleneck for next-gen systems, says Outshift by Cisco\u2019s SVP and GM Vijoy Pandey.<\/p>\n

As he describes the current state of AI: Agents can be stitched together in a workflow or plug into a supervisor model \u2014 but there's no semantic alignment, no shared context. They\u2019re essentially working from scratch each go-around.\u00a0<\/p>\n

This calls for next-level infrastructure, or what Pandey describes as the "internet of cognition."\u00a0<\/p>\n

\u201cAgents are not able to think together because connection is not cognition,\u201d he said. \u201cWe need to get to a point where you are sharing cognition. That is the greater unlock.\u201d<\/p>\n

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Creating new protocols to support next-gen agent communication<\/b><\/h2>\n

So what is shared cognition? It\u2019s when AI agents or entities can meaningfully work together to solve for something net new that they weren\u2019t trained for, and do it \u201c100% without human intervention,\u201d Pandey said on the latest episode of Beyond the Pilot.<\/p>\n

The Cisco exec analogizes it to human intelligence. Humans evolved over hundreds of thousands of years, first becoming intelligent individually, then communicating on a basic level (with gestures or drawings). That communication improved over time, eventually unlocking a \u2018cognitive revolution\u2019 and collective intelligence that allowed for shared intent and the ability to coordinate, negotiate, and ground and discover information.\u00a0<\/p>\n

\u201cShared intent, shared context, collective innovation: That's the exact trajectory that's playing out in silicon today,\u201d Pandey said.\u00a0<\/p>\n

His team sees it as a \u201chorizontal distributed assistance problem.\u201d They are pursuing \u201cdistributed super intelligence\u201d by codifying intent, context, and collective innovation as a set of rules, APIs, and capabilities within the infrastructure itself.\u00a0<\/p>\n

Their approach is a set of new protocols: Semantic State Transfer Protocol (SSTP); Latent Space Transfer Protocol (LSTP); and Compressed State Transfer Protocol (CSTP).\u00a0<\/p>\n

SSTP operates at the language level, analyzing semantic communication so systems can infer the right tool or task. Pandey's team recently collaborated with MIT on a related piece called the Ripple Effect Protocol.<\/p>\n

LSTP can be used to transfer the \u201centire latent space\u201d of one agent to another, Pandey explained. \u201cCan we just take the KV cache and send it over as an example?\u201d he said. \u201cBecause that would be the most efficient way: instead of going through the tax of tokenizing it, going to a natural language, then going back the stack on the other side.\u201d\u00a0<\/p>\n

CSTP handles compression \u2014 grounding only the targeted variants while compressing everything else. Pandey says it's particularly well-suited for edge deployments where you need to send large amounts of state accurately.<\/p>\n

Ultimately, Pandey\u2019s team is building a fabric to scale out intelligence and ensure that cognition states are synchronized across endpoints. Further, they are developing what they call \u201ccognition engines\u201d that provide guardrails and accelerate systems.\u00a0<\/p>\n

\u201cProtocols, fabric, cognition engines: These are the three layers that we are building out in the pursuit of distributed super intelligence,\u201d Pandey said.\u00a0<\/p>\n

How Cisco solved a big pain point<\/b><\/h2>\n

Stepping back from these advanced, next-level systems, Cisco has achieved tangible results with existing AI capabilities. Pandey described a specific pain point with the company\u2019s site reliability engineering (SRE) team.\u00a0<\/p>\n

While they were churning out more and more products and code, the team itself wasn\u2019t growing, and were feeling pressure to improve efficiency. Pandey introduced AI agents that automated more than a dozen end-to-end workflows, including continuous integration\/continuous delivery CI\/CD pipelines, EC2 instance spin-ups and Kubernetes cluster deployments.\u00a0<\/p>\n

Now, more than 20 agents \u2014 some built in-house, some third-party \u2014 have access to 100-plus tools via frameworks like Model Context Protocol (MCP), while also plugging into Cisco\u2019s security platforms.\u00a0<\/p>\n

The result: A decrease from \u201chours and hours to seconds\u201d with certain deployments; further, agents have reduced 80% of the issues the SRE team were seeing within Kubernetes workflows.<\/p>\n

Still, as Pandey noted, AI is a tool like any other. \u201cIt does not mean that I have a new hammer and I'm just gonna go around looking for nails,\u201d he said. \u201cYou still have deterministic code. You need to marry these two worlds to get the best outcome for the problem that you're solving.\u201d<\/p>\n

Listen to the podcast to hear more about:\u00a0<\/b><\/p>\n