Enterprise AI has a dirty secret: 96% of C-suite leaders expect it to boost productivity, but 77% of workers report it has increased their workload. The culprit isn't the models—it's the infrastructure. Organizations are running world-class AI on systems that burn millions of tokens answering the same questions, surfacing the wrong context, and forcing employees to fact-check every response.
DevRev's latest release of Computer, announced May 21, addresses this head-on with a "shared memory" architecture that delivers both speed and clarity. The result: lower token costs, trusted answers on the first try, and AI that acts like a teammate instead of a liability.
The Productivity Paradox: Speed Without Context
The AI industry has been selling speed. Faster answers. More outputs. Higher token throughput. But according to the Upwork Research Institute, this approach has backfired. While executives expect productivity gains, knowledge workers are drowning in noise.
The reason: context-free AI guesses instead of knows. Without a structured understanding of how your organization works—who owns what, what rules apply, what data is current—AI retrieves documents but doesn't understand applicability. A retrieved policy doesn't tell the agent whether it's expired, whether there's a conflicting rule, or whether it only applies in certain jurisdictions.
"Speed without the right context is just faster noise," said Dheeraj Pandey, Co-founder and CEO at DevRev. "Noise that overloads humans in the loop, and eventually breaks them."
This is "token maxxing"—applying more compute to inadequate contextual data. The result: employees lose hours hunting for information across disconnected systems, and the people meant to benefit from AI are left holding the bill for its failures.
Why RAG Isn't Enough for Enterprise Decision-Making
Retrieval-Augmented Generation (RAG) architectures are good at one thing: surfacing semantically relevant documents. That's also where they stop.
Enterprise context is sprawled across ERP tools, logs, databases, vector stores, and policy documents. RAG can retrieve from all of it—through keyword search, SQL queries, or full pipelines—but retrieval has a ceiling.
Retrieved documents don't equal decision context. Data may not be relevant to the decision at hand (causing hallucinations), and even if agents pull the right data, they often lack guidance to make decisions backed by a strong rationale.
"Everyone starts with RAG: Pull relevant docs, stuff them in the prompt, let the model figure it out," said Wyatt Mayham of Northwest AI Consulting. While that works fine for chatbots, it "breaks immediately" for agents that need to make decisions and take actions.
A retrieved document doesn't tell the agent:
- Whether it still applies
- Whether it's been superseded
- Whether there's a conflicting rule that takes priority
- What the timeline is for applicability
In construction, that might mean knowing that a pricing exception expired, that a safety policy only applies in certain jurisdictions, or that a standard operating procedure was updated a month prior. "Miss any of that, and the agent confidently does the wrong thing," Mayham said.
Without structured decision context, agents combine incompatible rules, invent constraints to fill gaps, and rely on what Rippletide co-founder Yann Bilien calls "probabilistic guesses over unbounded data." Errors are difficult to reproduce because builders can't trace why the agent made a given choice.
The compounding error problem is real. A small miss rate per step becomes catastrophic across a multi-step workflow. "That's the main reason most enterprise agents never leave the pilot phase," Mayham said.
How Shared Memory Solves the Context Problem
DevRev's Computer is built on a different philosophy: shared memory. This is a curated, living picture of an organization's data, how the organization works, and how its people interact.
Shared memory operates at three levels:
1. Individual Memory
Computer learns how each person works, picking up where they left off with each new session. Previous queries, preferred workflows, and context-specific decisions are retained.
2. Team Memory
The skills and AI agents one person develops become available to everyone. When a top-performing sales rep builds a competitive analysis workflow, the entire team gains access. When that rep leaves, their account knowledge stays in the system permanently.
3. Organizational Memory
Institutional knowledge stays in the system permanently. Rules, exceptions, and decision paths are encoded so agents know what applies right now—not just what's semantically similar.
Technical Foundation: Precision, Efficiency, Safety
Shared memory enables three capabilities that RAG architectures struggle to deliver:
Precision
Answers sourced from real business data, cited and referenced. Same question, same answer, every time. Computer doesn't guess—it knows, and it shows its work.
This solves the hallucination problem. Instead of probabilistic retrieval, Computer explicitly encodes applicability: what rules to remember, what context applies in a given situation, and what was true then versus what is true now.
Efficiency
Trusted answers at lower cost, lower token usage, no analyst required. Full context on the first response eliminates the back-and-forth that burns tokens without progress.
57% of employees admit to using AI in non-transparent ways, including avoiding revealing when they used AI tools to complete their work, according to a 2025 KPMG and University of Melbourne study. Teams rarely benefit from one another's AI work.
DevRev's Multiplayer AI changes this. Teams share a live Computer session where everyone sees the full context and continues the analysis together. Colleagues can question, build on, and correct reasoning in real time. The unit of attribution shifts from "what I did with AI" to "what we did with AI, together."
Safety
Nothing goes out before a human approves it. Full audit trails and the ability to undo any agent action mean enterprises can move faster without risk.
Agent Studio gives any team the ability to build, test in a sandbox environment, and deploy AI agents that take action across connected systems. Every action runs under individual user permissions, not a shared account. Every step is traceable, auditable, and reversible. If an agent makes a mistake, it can be rolled back.
The New Desktop App: From Answers to Action
Earlier versions of Computer answered questions and took single-step actions. The addition of the new desktop app shifts Computer from a question-answering tool to a content-producing system.
From the in-app canvas, any user can generate complete, fully branded and formatted work artifacts grounded in real business data:
- Competitive slide decks
- QBR reports
- Structured dashboards
- Knowledge base articles
- Multi-step workflows
These outputs are available in PPT, HTML, PDF, DOCX, and more. Skills and outputs built in the Canvas are saved at the user, team, or organization level and become reusable across the business.
Production Results: 250+ Organizations Already Using Computer
More than 250 organizations have Computer in production. Key capabilities now generally available:
Shared Memory: Personal, team, and organizational memory that compounds over time
Trusted Answers: Intent-aware search and consistent data answers. Same question, same answer, every time.
Safe Actions: Governed, auditable actions across systems. Agents move faster without risk.
Multiplayer AI: Shared live sessions for human-to-human, human-to-AI, and team-wide collaboration
Skills: Reusable workflows that any team can build, share, and deploy. One person's expertise becomes everyone's capability.
Agent Studio: Build and deploy AI agents with sandboxed testing, full audit trails, and rollback
Text2SQL: Analytical queries across structured data in plain language, no data analyst required
Connectors: Gmail, Outlook, Slack, Notion, Google Drive, Jira, Microsoft OneDrive, SharePoint, and any MCP-compatible tool
Usage-based pricing: Scales with adoption, not headcount
What This Means for Enterprise Leaders
For CTOs and CIOs: Shared memory architecture addresses the fundamental limitation of RAG—context applicability. This isn't about retrieval speed; it's about decision quality. If your enterprise agents are stuck in pilot purgatory because they can't compound on validated behaviors, you need a different architectural foundation.
For CFOs and business leaders: The productivity paradox is real. 96% of executives expect gains, but 77% of workers report more work. The gap is context. AI without shared memory burns tokens answering the same questions, forces employees to fact-check everything, and turns productivity tools into productivity drains. Usage-based pricing means you pay for value delivered, not seats purchased.
For compliance and legal leaders: Auditability and rollback capabilities are non-negotiable for regulated industries. Agent Studio's sandbox testing, full audit trails, and individual user permissions mean you can move fast without exposing the organization to uncontrolled risk.
The Bottom Line
Enterprise AI has been optimizing for the wrong metric. Speed is useless if it's speed toward the wrong answer. Token volume is irrelevant if you're burning millions to re-answer questions the system should already know.
Shared memory changes the equation: lower costs, trusted answers on the first try, and AI that compounds institutional knowledge instead of guessing every time.
The question for enterprise leaders: Are you building on context-free infrastructure, or are you building systems that remember?
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