AI Planning and Design

 As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.”

To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a new business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.”

There is a category of production incident that engineering teams are not tracking yet — because it doesn’t fit any existing postmortem template.

The agent initiated an action. The action was technically correct given the agent’s context. The context was incomplete. The infrastructure cascaded. And, by the time the incident review happened, three teams were arguing about whether it was an agent failure or an infrastructure failure,  because the frameworks for thinking about these two things have never been connected.

The scale of this exposure is no longer theoretical. Seventy-nine percent of organizations now have some form of AI agent in production, with 96% planning expansion. Gartner predicts 33% of enterprise software will include agentic AI by 2028, but separately warns that 40% of those projects will be canceled due to poor risk controls.

What neither statistic captures is the failure mode happening between those two numbers: Agents that are running, that are not canceled, and that are quietly generating infrastructure events no one has categorized as risk.

A familiar warning now shapes much of the discussion about artificial intelligence: A handful of dominant firms will control the technologies, stifle innovation, and require aggressive antitrust intervention. It is a compelling story—and mostly wrong.

The idea that large companies automatically mean less innovation has become conventional wisdom in antitrust circles. European regulators have embraced it, blocking mergers and attacking American tech companies. The Biden administration followed that path, treating size itself as a threat and wanting government-led AI. The Trump administration, by contrast, has signaled a more evidence-based view—one grounded in both economic logic and empirical studies.

The ‘cloud-native’ architecture of the last decade is built on a 20-year-old assumption: that state
lives in the database, and compute is stateless. If you want to scale, you scale the database
vertically (get a larger machine) ^[1][1] or design the database schema around partition the data
and you scale your application servers horizontally (add more
boxes). Any request can hit any server, the loadbalancer doesn’t care, and the database is the
single source of truth.

LLMs and agents are quietly violating this assumption, and making this architecture increasingly
hard to work with. Not all at once, but in three subtle ways: