- Your Personal Knowledge Graph Now Lives Outside the Control Plane
Enterprise AI teams running centralized orchestration stacks now have a new variable to account for: AWS Quick, which expanded this week to a desktop-native agent that builds a persistent personal knowledge graph and executes actions across local files and SaaS tools — outside the visibility of most control planes.
Unlike chat-based copilots that reset with each session, Quick now maintains a continuously updated knowledge graph built from the user's local files, calendar, email and connected SaaS apps. It uses it to proactively trigger actions without waiting to be asked.
AWS launched Quick in October last year as an alternative to AI workflow and productivity platforms coming from Google, OpenAI and Anthropic. It was a way for enterprise employees to access insights from connected applications, an agent builder, deep research, and workflow automation. Now, it’s grown beyond a simple AI assistant and acts more as a proactive workflow agent with a stateful, real-time knowledge graph of the user. It integrates with third-party apps like Google Workspace, Microsoft 365, Zoom, Salesforce and Slack — and now local files — so the agent can gather context and take actions.
“What we’ve been hearing is that many enterprises have not been happy with how difficult it is to get context from their legacy tools,” Jigar Thakkar, vice president of Quick Suite at AWS, told VentureBeat in an interview. “Our vision is that Quick is a desktop experience that is the one place where people can go to get all their information and tasks.”
Governance blindspots
Enterprises often put orchestration layers at the center to help guide and manage agents. Context is pulled in, decisions are made, and then actions are executed within defined system boundaries.
Recent releases like Anthropic’s Claude Managed Agents or updates to OpenAI’s Agent SDK also push for more stateless, autonomous agents within enterprise workflows, but still operate within defined orchestration boundaries.
Quick still operates under enterprise controls, something that AWS has always underscored with its AI products, so actions taken on Quick remain bound by permissions, identity and security. Integrations remain managed by either an API or an MCP connection.
However, this evolution of Quick introduces a more subtle shift in the decision layer. AWS updated Quick to build a personal knowledge graph that learns more about the user the more they interact with the platform. It builds a profile based on how they use local files, calendar, email or third-party app integrations to proactively suggest actions such as reminding a team leader to set up check-ins.
Enterprises should be wary that a kind of shadow orchestration could arise in a system like this. The personalized context means the decision layer focuses on implicit triggers rather than set workflows, user-specific interpretations, and different action timings. Practitioners are rightfully wary of this much autonomy, understanding that shadow orchestration may not be something completely under their control.
Upal Saha, co-founder and CTO of Bem, told VentureBeat in an email that platforms like AWS Bedrock AgentCore, its managed agent runtime, and similar ones from Salesforce "maximize autonomy rather than accountability" so enterprises are not losing agent visibility by accident.
"When you deploy an agent that reasons its way to a decision across multiple steps, you have already accepted that you will not be able to fully explain what happened after the fact," Saha said. "That is fine for a demo. It is not fine for a claims processing pipeline or a financial workflow where a regulator can ask you to produce a complete audit trail for every automated decision made in the last three years."
AWS said the platform's governance model is designed to address these concerns. “Users can set up different agents and automated workflows tailored to their role — things like monitoring tickets, pulling data from connected systems, or drafting docs — all managed within a governed environment where IT retains control over what's connected and what data flows where. It's designed to give individual users flexibility while keeping enterprise-level oversight in place,” an AWS spokesperson said.
A possible blueprint
Quick’s evolution from an AI assistant to something more proactive represents a possible approach some enterprise software providers will take to deep AI agent integration into workflows. While what AWS wants to accomplish with Quick—better context from apps and local files and a strong understanding of what its users actually want to do—is not unique, it isn’t focusing on traditional orchestration. Instead, it’s relying on context-driven agent management.
This market tension is growing, as evidenced by the release of similar platforms. Mistral, for example, announced Workflows the same day as the updates to Quick. That platform uses a more traditional orchestration framework.
Stateful and personalized agents continue to evolve, and so do the questions around how enterprises govern them.
- The real AI bottleneck is agents that can't share context.
AI agents can connect together, but they cannot think together. That’s a huge difference and a bottleneck for next-gen systems, says Outshift by Cisco’s SVP and GM Vijoy Pandey.
As he describes the current state of AI: Agents can be stitched together in a workflow or plug into a supervisor model — but there's no semantic alignment, no shared context. They’re essentially working from scratch each go-around.
This calls for next-level infrastructure, or what Pandey describes as the "internet of cognition."
“Agents are not able to think together because connection is not cognition,” he said. “We need to get to a point where you are sharing cognition. That is the greater unlock.”
Creating new protocols to support next-gen agent communication
So what is shared cognition? It’s when AI agents or entities can meaningfully work together to solve for something net new that they weren’t trained for, and do it “100% without human intervention,” Pandey said on the latest episode of Beyond the Pilot.
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 ‘cognitive revolution’ and collective intelligence that allowed for shared intent and the ability to coordinate, negotiate, and ground and discover information.
“Shared intent, shared context, collective innovation: That's the exact trajectory that's playing out in silicon today,” Pandey said.
His team sees it as a “horizontal distributed assistance problem.” They are pursuing “distributed super intelligence” by codifying intent, context, and collective innovation as a set of rules, APIs, and capabilities within the infrastructure itself.
Their approach is a set of new protocols: Semantic State Transfer Protocol (SSTP); Latent Space Transfer Protocol (LSTP); and Compressed State Transfer Protocol (CSTP).
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.
LSTP can be used to transfer the “entire latent space” of one agent to another, Pandey explained. “Can we just take the KV cache and send it over as an example?” he said. “Because 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.”
CSTP handles compression — 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.
Ultimately, Pandey’s 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 “cognition engines” that provide guardrails and accelerate systems.
“Protocols, fabric, cognition engines: These are the three layers that we are building out in the pursuit of distributed super intelligence,” Pandey said.
How Cisco solved a big pain point
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’s site reliability engineering (SRE) team.
While they were churning out more and more products and code, the team itself wasn’t 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.
Now, more than 20 agents — some built in-house, some third-party — have access to 100-plus tools via frameworks like Model Context Protocol (MCP), while also plugging into Cisco’s security platforms.
The result: A decrease from “hours and hours to seconds” with certain deployments; further, agents have reduced 80% of the issues the SRE team were seeing within Kubernetes workflows.
Still, as Pandey noted, AI is a tool like any other. “It does not mean that I have a new hammer and I'm just gonna go around looking for nails,” he said. “You still have deterministic code. You need to marry these two worlds to get the best outcome for the problem that you're solving.”
Listen to the podcast to hear more about:
How we are now enabling a new paradigm of non-deterministic computing.
How Cisco bumped error detection capabilities in large networks from 10% to 100%.
How Pandey named his own AI agent Arnold Layne after an early Pink Floyd song.
Why the "internet of cognition" must be an open, interoperable effort.
How Cisco’s open source project Agntcy addresses discovery, identity and access management (IAM), observability, and evaluation.
You can also listen and subscribe to Beyond the Pilot on Spotify, Apple or wherever you get your podcasts.
- The Scaffolding Era Fades, Making Way for Smarter AI Workflows
The scaffolding layer that developers once needed to ship LLM applications — indexing layers, query engines, retrieval pipelines, carefully orchestrated agent loops — is collapsing. And according to Jerry Liu, co-founder and CEO of LlamaIndex, that's not a problem. It's the point.
“As a result, there's less of a need for frameworks to actually help users compose these deterministic workflows in a light and shallow manner,” Jerry Liu, co-founder and CEO of LlamaIndex, explains in a new VentureBeat Beyond the Pilot podcast.
Context is becoming the moat
Liu’s LlamaIndex is one of the foremost retrieval-augmented generation (RAG) frameworks connecting private, custom, and domain-specific data to LLMs. But even he acknowledges that these types of frameworks are becoming less relevant.
With every new release, models demonstrate incremental capabilities to reason over “massive amounts” of unstructured data, and they’re getting better at it than humans, he notes. They can be trusted to reason extensively, self-correct, and perform multi-step planning; Modern Context Protocol (MCP) and Claude Agent Skills plug-ins allow models to discover and use tools without requiring integrations for every one independently.
Agent patterns have consolidated toward what Liu calls a "managed agent diagram" — a harness layer combined with tools, MCP connectors, and skills plug-ins, rather than custom-built orchestration for every workflow.
Further, coding agents excel at writing code, meaning devs don’t need to rely on extensive libraries. In fact, about 95% of LlamaIndex code is generated by AI. “Engineers are not actually writing real code,” Liu said. “They're all typing in natural language.” This means the layers between programmers and non-programmers is collapsing, because “the new programming language is essentially English.”
Instead of manual coding or struggling to understand API and document integration, devs can just point Claude Code at it. “This type of stuff was either extremely inefficient or just would break the agent three years ago,” said Liu. “It's just way easier for people to build even relatively advanced retrieval with extremely simple primitives.”
So what’s the core differentiator when the stack collapses?
Context, Liu says. Agents need to be able to decipher file formats to extract the right information. Providing higher accuracy and cheaper parsing becomes key, and LlamaIndex is well-positioned here, he contends, because of its developments with agentic document processing via optical character recognition (OCR).
“We've really identified that there's a core set of data that has been locked up in all these file format containers,” he said. Ultimately, “whether you use OpenAI Codex or Claude Code doesn't really matter. The thing that they all need is context.”
Keeping stacks modular
There’s growing concern about builders like Anthropic locking in session data; in light of this, Liu emphasizes the importance of modularity and agnosticism. Builders shouldn’t bet on any one frontier model, or overbuild in a way that overcomplicates components of the stack.
Retrieval has evolved into “agent-plus-sandbox,” as he describes it, and enterprises must ensure that their code bases are tech debt free and adaptable to changing patterns. They also have to acknowledge that some parts of the stack will eventually need to be thrown away as a matter of course.
“Because with every new model release, there's always a different model that is kind of the winner,” Liu said. “You want to make sure you actually have some flexibility to take advantage of it.”
Listen to the podcast to hear more about:
LlamaIndex’s beginnings as a ‘toy project’ with initially only about 40% accuracy;
How SaaS companies can tap into complicated workflows that must be standardized and repeatable for average knowledge workers;
Why vertical AI companies are taking off and why ‘build versus buy’ is still a very valid question in the agent age.
You can also listen and subscribe to Beyond the Pilot on Spotify, Apple or wherever you get your podcasts.