The era of agentic chaos and how data will save us | MIT Technology Review

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Skip to ContentMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioMIT Technology ReviewFeaturedTopicsNewslettersEventsAudioSponsoredArtificial intelligenceThe era of agentic chaos and how data will save usAutonomous agents will soon run thousands of enterprise workflows, and only organizations with unified, trusted, context-rich data will prevent chaos and unlock reliable value at scale.
By Ansh Kanwararchive pageJanuary 20, 2026Provided byReltio AI agents are moving beyond coding assistants and customer service chatbots into the operational core of the enterprise. The ROI is promising, but autonomy without alignment is a recipe for chaos. Business leaders need to lay the essential foundations now. The agent explosion is coming Agents are independently handling end-to-end processes across lead generation, supply chain optimization, customer support, and financial reconciliation. A mid-sized organization could easily run 4,000 agents, each making decisions that affect revenue, compliance, and customer experience. 
The transformation toward an agent-driven enterprise is inevitable. The economic benefits are too significant to ignore, and the potential is becoming a reality faster than most predicted. The problem? Most businesses and their underlying infrastructure are not prepared for this shift. Early adopters have found unlocking AI initiatives at scale to be extremely challenging.  The reliability gap that’s holding AI back Companies are investing heavily in AI, but the returns aren't materializing. According to recent research from Boston Consulting Group, 60% of companies report minimal revenue and cost gains despite substantial investment. However, the leaders reported they achieved five times the revenue increases and three times the cost reductions. Clearly, there is a massive premium for being a leader. 
What separates the leaders from the pack isn't how much they're spending or which models they're using. Before scaling AI deployment, these “future-built” companies put critical data infrastructure capabilities in place. They invested in the foundational work that enables AI to function reliably.  A framework for agent reliability: The four quadrants To understand how and where enterprise AI can fail, consider four critical quadrants: models, tools, context, and governance. Take a simple example: an agent that orders you pizza. The model interprets your request ("get me a pizza"). The tool executes the action (calling the Domino's or Pizza Hut API). Context provides personalization (you tend to order pepperoni on Friday nights at 7pm). Governance validates the outcome (did the pizza actually arrive?).  Each dimension represents a potential failure point: Models: The underlying AI systems that interpret prompts, generate responses, and make predictions Tools: The integration layer that connects AI to enterprise systems, such as APIs, protocols, and connectors  Context: Before making decisions, information agents need to understand the full business picture, including customer histories, product catalogs, and supply chain networks Governance: The policies, controls, and processes that ensure data quality, security, and compliance This framework helps diagnose where reliability gaps emerge. When an enterprise agent fails, which quadrant is the problem? Is the model misunderstanding intent? Are the tools unavailable or broken? Is the context incomplete or contradictory? Or is there no mechanism to verify that the agent did what it was supposed to do? Why this is a data problem, not a model problem The temptation is to think that reliability will simply improve as models improve. Yet, model capability is advancing exponentially. The cost of inference has dropped nearly 900 times in three years, hallucination rates are on the decline, and AI’s capacity to perform long tasks doubles every six months. Tooling is also accelerating. Integration frameworks like the Model Context Protocol (MCP) make it dramatically easier to connect agents with enterprise systems and APIs. If models are powerful and tools are maturing, then what is holding back adoption?

To borrow from James Carville, “It is the data, stupid.” The root cause of most misbehaving agents is misaligned, inconsistent, or incomplete data. Enterprises have accumulated data debt over decades. Acquisitions, custom systems, departmental tools, and shadow IT have left data scattered across silos that rarely agree. Support systems do not match what is in marketing systems. Supplier data is duplicated across finance, procurement, and logistics. Locations have multiple representations depending on the source. Drop a few agents into this environment, and they will perform wonderfully at first, because each one is given a curated set of systems to call. Add more agents and the cracks grow, as each one builds its own fragment of truth. This dynamic has played out before. When business intelligence became self-serve, everyone started creating dashboards. Productivity soared, reports failed to match. Now imagine that phenomenon not in static dashboards, but in AI agents that can take action. With agents, data inconsistency produces real business consequences, not just debates among departments. Companies that build unified context and robust governance can deploy thousands of agents with confidence, knowing they'll work together coherently and comply with business rules. Companies that skip this foundational work will watch their agents produce contradictory results, violate policies, and ultimately erode trust faster than they create value. Leverage agentic AI without the chaos  The question for enterprises centers on organizational readiness. Will your company prepare the data foundation needed to make agent transformation work? Or will you spend years debugging agents, one issue at a time, forever chasing problems that originate in infrastructure you never built? Autonomous agents are already transforming how work gets done. But the enterprise will only experience the upside if those systems operate from the same truth. This ensures that when agents reason, plan, and act, they do so based on accurate, consistent, and up-to-date information.  The companies generating value from AI today have built on fit-for-purpose data foundations. They recognized early that in an agentic world, data functions as essential infrastructure. A solid data foundation is what turns experimentation into dependable operations.
At Reltio, the focus is on building that foundation. The Reltio data management platform unifies core data from across the enterprise, giving every agent immediate access to the same business context. This unified approach enables enterprises to move faster, act smarter, and unlock the full value of AI. Agents will define the future of the enterprise. Context intelligence will determine who leads it. For leaders navigating this next wave of transformation, see Relatio’s practical guide:Unlocking Agentic AI: A Business Playbook for Data Readiness. Get your copy now to learn how real-time context becomes the decisive advantage in the age of intelligence.  by Ansh KanwarShareShare story on linkedinShare story on facebookShare story on emailPopular10 Breakthrough Technologies 2026Amy NordrumThe great AI hype correction of 2025Will Douglas HeavenChina figured out how to sell EVs. Now it has to deal with their aging batteries.Caiwei ChenThe 8 worst technology flops of 2025Antonio RegaladoDeep DiveArtificial intelligenceThe great AI hype correction of 2025Four ways to think about this year's reckoning.
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The era of agentic chaos hinges on a unified and trusted data foundation, according to Ansh Kanwar’s analysis for MIT Technology Review. The impending “agent explosion,” characterized by thousands of autonomously operating agents handling enterprise workflows, will only succeed if organizations establish robust data infrastructure. The core challenge lies in navigating the inherent risks of deploying these agents without a coherent and reliable data source.

The article outlines a critical framework for understanding agent reliability, broken down into four quadrants: models, tools, context, and governance. Models represent the AI systems themselves—their ability to interpret prompts and generate accurate responses is paramount. Tools provide the integration layer, connecting agents to existing enterprise systems via APIs and protocols. Context refers to the information agents require to make informed decisions, encompassing data from customer histories, product catalogs, and supply chains. Finally, governance establishes policies and controls, ensuring data quality, security, and compliance.

Kanwar emphasizes that the primary cause of agent misbehavior isn't necessarily flawed models, but rather misaligned, inconsistent, or incomplete data. Decades of accumulated data “debt” – resulting from acquisitions, disparate systems, and shadow IT – have created fragmented data landscapes. This is exacerbated by the deployment of agents, which, without a unified context, will quickly reveal cracks and contradictions. Businesses that skip this foundational work risk a chaotic environment where agents produce conflicting results and erode trust.

The article argues that leaders have already begun to recognize the importance of data readiness. Those who proactively build “fit-for-purpose” data foundations—understanding that data functions as essential infrastructure—will be best positioned to deploy and manage fleets of agents. Reltio, as presented in the article, is focused on providing this unified data management platform, enabling immediate access to the same business context for all agents.

Ultimately, the success of agentic AI depends on transforming raw data into actionable intelligence. It’s a system where “context intelligence” will determine who leads in this new era of technological transformation. The article’s overarching message is a call to action for businesses to prioritize data readiness and build robust foundations for a future powered by autonomous agents.