The Agentic Workspace Layer Unfolds
Enterprise AI is moving beyond assistive use cases, with agents increasingly expected to plan, coordinate, and carry out complex workflows across enterprise systems. Simply put: If the first wave of enterprise AI was defined by chatbots answering questions, the second wave, the one unfolding right now, is about getting things done.
This shift is underway across boardrooms and engineering or development teams. AI systems are no longer just drafting emails or summarising meetings. They are being asked to plan, orchestrate, and execute multi-step workflows across entire enterprise tool stacks.
For instance, Microsoft’s Copilot uses real-time signals from Outlook, Teams and Excel to ensure context-aware actions. It also leverages real-world data (like an unread email or a mention in a chat) to remind a user of a pending action and then builds tailored outputs.
Similarly, Google is enabling AI agents to interact with its productivity suite through programmable interfaces. A programmed AI agent can now enter a Google Drive, open a document and edit it — just like a human, but much faster.
Beyond big techs, a swarm of startups is creating AI conduits that no longer just offer assistance but can execute multi-step workflows.
“The workplace itself is becoming the operating environment where agents plan and execute work… What the industry is building is not simply smarter assistants. It is a new execution fabric that sits between human intent and enterprise systems,” said Greyhound Research CEO Sanchit Vir Gogia.
Taken together, a new layer of enterprise AI architecture is forming, one that could prove more consequential than the historic transition to the cloud. But how is this new enterprise AI stack taking shape?
From Assistance To Execution
The paradigm is moving from AI that helps you work to AI that works for you. The engineering implications of this shift are massive, requiring a multi-layered approach to handle the complexity of the modern office.
Gogia breaks this down into four layers:
- Intent interface layer: Where users describe outcomes
- Planning layer: Where agents decompose goals into action sequences
- Execution runtime layer: Invokes APIs and calling tools, and interacts with enterprise applications.
- Control layer: This is responsible for identity, governance, and auditability
Google’s Workspace CLI serves as a prime example of this infrastructure in motion. It dynamically generates commands from Google’s API discovery services, allowing agents to interact with Gmail, Drive, and Sheets on a user’s behalf without custom integrations.
According to Google Cloud, the vision extends well beyond developer tooling. With Workspace Flows, enterprises can automate work across apps autonomously using plain language.
Through Workspace Studio, any employee can build agents using natural language that orchestrate work across Gmail, drive, and chat. These agents can understand the full context of work across the organisation, matching company policies and generating content in the user’s own tone and style.
The ambition is to give agents a unified view of ‘enterprise truth’ — understanding not just one document, but the relationships between emails, chats, CRM data, and project trackers across the entire organisation.
For Pratik Jain, senior director of technology at Kyvos Insights, “The whole concept of Cowork [Claude’s agentic AI assistant] is that just like humans as their coworkers, you give them a very high-level guidance and then based on their skills and profiles, they deliver.”
As an example for his company, an integration with Claude’s Copilot Cowork illustrates how the broader ecosystem is taking shape — agents connecting to specialised tools through plugin architectures, each component contributing a specific capability to a workflow that no single system could execute alone.
Ankush Sabharwal, founder and CEO of CoRover and BharatGPT, said, “The biggest challenge is moving from AI that suggests actions to AI that reliably executes them. Enterprise workflows require deterministic outcomes, while large language models are inherently probabilistic.”
He argues that agents need secure API orchestration, contextual understanding of enterprise data, strong identity and access controls, and workflow reliability layers to truly operate across enterprise software stacks.
The numbers also reflect how early the market still is. Industry studies cited by Sabharwal show that more than 60% of enterprises are exploring AI agents, but less than a quarter have scaled them into production workflows.
Tasks that once required five business days, like pulling data, building dashboards, and iterating on analysis, can now be done in hours. “The time to analyse and the time to reach a conclusion is way shorter,” Jain said.
Reliability, Guardrails, And the Trust Gap
While the demos of these execution-led AI agents look compelling, they stumble in production. Greyhound Research’s analysis identifies a persistent gap between what agent systems promise and what they deliver in live enterprise environments. Agents often fail when interacting with dynamic systems where interfaces shift, data schemas change, or permissions vary. When agents operate through iterative reasoning loops, small errors compound quickly.
“The root problem is not model capability, but that enterprise systems themselves are complex and unpredictable,” Gogia noted.
To counter this, a new solution is emerging in the form of “hybrid orchestration” – language models that plan and coordinate, while sitting atop the deterministic automation infrastructure that handles the actual execution.
Jain noted that human intervention remains the primary safeguard. Therefore, Kyvos restricts delete commands and tests agentic workflows within sandboxed environments to maintain control.
Here, Sabharwal of CoRover identifies three non-negotiables:
- Role-based permissions so agents operate within defined policies
- Full auditability and traceability of every action
- Human-in-the-loop oversight, especially for high-impact decisions.
“The future enterprise AI stack is not just required to be intelligent, but also transparent, controllable, and compliant by design,” he added
Sonica Aron, founder and managing partner of Marching Sheep, added a workforce dimension to the debate. “While the efficiency and productivity seem extremely attractive, organisations should not adopt them at scale without strong guardrails.”
She argues that AI agents will likely shift employees from execution to supervision roles.
Gogia flags a subtler but growing security risk. He said prompt injection attacks, where malicious instructions embedded in emails, documents, or tickets are interpreted as legitimate agent commands.
Agents Are The New Enterprise OS
Whoever owns the agentic workspace layer may effectively own the operating system for knowledge work and, with it, a new form of enterprise lock-in more complex than anything the SaaS era produced.
Gogia argues that lock-in risks stems from operational ecosystems — libraries, governance, and connectors — rather than the models themselves. As these layers integrate, switching costs become compounding.
Sabharwal envisions a future of interoperable, multi-model AI ecosystems over closed platforms. To ensure enterprise flexibility, CoRover focuses on open infrastructure using specialised small language models and a conversational interface for building and deploying agents across diverse stacks.
Aron echoes the governance argument from the enterprise adoption side: “The companies that implement these governance structures early will be able to capture productivity gains without exposing themselves to operational or reputational risk.”
On whether to automate everything, Jain said, “It’s always where you’re investing more human resources where you can save their cost — those become the primary point of implementing AI. Not every workflow needs an agent. If things are working, don’t break it.”
The productivity war for the agentic workspace layer has begun. While tech giants and startups are building the infrastructure to enable this shift — from unified data layers to agent orchestration frameworks — reliability gaps, security risks, and governance challenges continue to slow adoption, even as the promise of significant productivity gains drives experimentation.
Top Stories From India & Around The World
- Mozark Raises $40 Mn: Singapore-headquartered SaaS startup Mozark raised a Series B round led by IFC and RMB Capitalworks. The company helps businesses test and monitor real-world performance in application testing, network performance, and digital life monitoring
- Coreworks AI Raises $5 Mn: Bengaluru and San Francisco-based Coreworks AI raised seed funding led by Together Fund. Its SuperAnalyst platform connects with ERPs, CRMs, financial models, and spreadsheets to automatically generate board presentations, financial reports, and written insights using a multi-agent system
- Constems-AI Raises $2 Mn: Deeptech startup Constems-AI raised pre-Series A funding led by Finvolve. Its CAInatics platform analyses retail shelf images to automate product placement tracking and merchandising compliance for Fortune 500 CPG companies
- Anthropic Launches The Anthropic Institute: Anthropic launched a new research body led by cofounder Jack Clark to study AI’s impact on socioeconomic and legal spheres. Among other objectives, the group will study how AI systems will reshape jobs, economies along with the opportunities and risks in terms of societal resilience
- Mira Murati’s Thinking Machines Lab Partners With NVIDIA: NVIDIA and Thinking Machines Lab announced a multi-year partnership to deploy at least one gigawatt of next-generation NVIDIA Vera Rubin systems for frontier model training. Former OpenAI exec Murati’s startup also raised a new round led by NVIDIA.
The Weekly Buzz: Vibe-Coding Palantir
“This guy vibe-coded Palantir” — that was the reaction of some when Bilawal Sidhu, a former Google product manager, decided to pull together open source intelligence and publicly available data to create what he called WorldView.
It all started with Sidhu’s itch to follow the events in Iran, the Middle East and Israel in real-time, instead of just following the news. So he set out to build exactly that over a weekend.
WorldView visualises openly available information, commercial flight paths, satellite positions, GPS signal quality, ship movements, and airspace closures onto a live 3D globe. None of this data is classified. By stacking it all together on the same timeline, he got a near-real-time picture of what happened, and without requiring any security clearance.
Sidhu mapped over 3,400 flights rerouting around Iran and the Gulf countries as well as spy satellites from the US, Russia, and China passing over the conflict regions.
What makes this remarkable isn’t just what he built — it’s how fast Sidhu managed to pull this off. As he himself says in this video, a project like this would have taken a full engineering team an entire quarter just a few years ago. By directing AI agents to snapshot every data feed before the caches cleared, he was able to pull it off in a matter of 48 hours.
This is why we won’t rule out the possibility of other such tools mimicking the same functionality coming soon, but Sidhu intends to release WorldView as a public tool in April.
Startup In The Spotlight: Adnoxy
A large part of India’s ad industry still relies on manual media planning, decisions that are not data-backed and often this results in limited visibility for brands on their return on investments. Measuring ROI, forecasting performance and optimising ad placements is a serious challenge in billboards, out-of-home advertising and other offline channels.
Adnoxy, founded in 2025 by Naman Sanghi and Aman Bansiwal, is looking to plug this gap with an AI-driven forecasting and analytics engine designed specifically for offline advertising. The platform focuses on modelling what the founders describe as “attention physics” — analysing factors such as dwell time, visibility, clutter and audience targeting to predict how offline ads will perform.
At its core, Adnoxy leverages predictive analytics fine-tuned on campaign data and physical advertising parameters to help brands identify optimal inventory and placements. In theory, this could help advertisers and media planners move beyond impression-based planning, and focus on measurable outcomes and returns.
The Gurugram-based bootstrapped is targeting D2C brands for its initial go-to-market run, with plans to expand across sectors such as real estate, entertainment and fintech as its models scale.
Prompt of The Week
What prompts and hacks are CTOs, CEOs and cofounders using these days to streamline their work?
Here’s Tarun Nazare, cofounder and managing director of Neokred, on analysing payments hardware strategy in India and global markets:
“Analyse the key features of UPI soundbox and POS devices in India, as well as comparable payment hardware in global markets.
Identify which hardware features (for example, connectivity options, UI feedback mechanisms, security modules, biometric support, integration with payment APIs, offline modes) are most widely adopted by payment providers and why.”
Highlight:
— Feature trends, user benefits, and gaps between domestic and international implementations that could inform product strategy for payments hardware.
Editor’s Note: Some prompts may need to be adjusted by users for best results or may not work as intended for certain users.
Edited by: Shishir Parasher
Creatives by: Abhyam Gusai
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