A recent market analysis by Jason Saltzman sparked some thinking…
The current discourse around AI is fixated on the “Brain” – Large Language Models (LLMs) and the generative capabilities of agents. But for the enterprise, a brain without a nervous system can quickly go from a dream to a nightmare.
As enterprises move toward Agentic AI, we are seeing a dangerous trend: trying to run every event through an LLM or using agents only for “manual” reconciliatory tasks. This approach is economically unsustainable, hallucination-prone, and a major compliance/regulation red flag.
To achieve that, a new blueprint is emerging: Determinism-First Agentic Operations.
From Passive Storage to Active, Real-Time Decisioning
Traditional databases have primarily functioned as systems of record. They store history, reconcile transactions, and support analytics.
Agentic AI changes that model.
AI agents interact with live systems. They influence state changes in real time. In these environments, latency, consistency, and transactional integrity are inseparable.
The platforms that win in this new era will not simply be AI-ready. They will be capable of executing deterministic, low-latency, ACID-compliant decisions at scale.
This requires a streaming transactional data platform that can:
- Process high-velocity event streams
- Maintain strict ACID compliance
- Enforce policy in real time
- Guarantee consistent state management
Determinism becomes foundational.
Three Proving Grounds for Deterministic, Streaming Decisioning
The transition from passive data storage to active, real-time decisioning is already visible in three high-stakes arenas:
1. Financial Services: The End of Post-Transaction Fraud Detection
In global finance, a “reconciliatory” agent is a failed agent. If an AI agent only flags fraud after the transaction, the capital has already left the building. The new requirement is In-Event Decisioning. This requires a data platform that unifies stateful stream processing with a database to authorize streaming transactions, run a fraud model, and maintain strict ACID compliance within milliseconds.
2. 5G & Telco: The Intelligent, Autonomous Network
5G isn’t just a bigger pipe; it’s a million simultaneous decisions. From real-time network slicing to millisecond-accurate billing and charging to adaptive network management, the “workflow” is a high-velocity stream of events. A consistency error in a 5G network doesn’t just result in a slow query -it results in dropped revenue and systemic failure. Agentic AI here must be deterministic and operate within the strict guardrails of transactional integrity.
3. The Edge: Agentic Autonomy at the Point of Impact
Whether it’s a smart grid or an automated factory floor, the Edge is where the “round-trip to the cloud” ends. AI agents at the edge must make split-second decisions based on real-time sensor data. You cannot drive an autonomous system on a database that only “records history.” You need an engine that makes explainable decisions and executes actions.
The Case for Determinism-First AI
An AI-first architecture is inherently probabilistic. That is appropriate for creative exploration and pattern discovery.
It is not sufficient for financial ledgers, telecom billing systems, or industrial control platforms.
A Determinism-First architecture ensures that the foundational elements of a transaction are handled by a deterministic execution layer:
- State changes
- Balance updates
- Policy enforcement
- ACID-compliant transactional integrity
This deterministic layer becomes the ground truth upon which AI agents can safely reason.
In production AI systems, intelligence must be bounded by infrastructure.
The DAD Model: Determinism–Agency–Determinism
Agentic AI needs to be sandwiched between deterministic layers. Deterministic streaming transactional decisioning enables a very high percentage of events to meet latency SLAs and can opportunistically escalate to AI agents for exceptions/mixed signals. When agents require information to support their planning and reasoning for recommendations, rather than receiving raw data via database access, they would be better served by calling intelligent APIs to base their reasoning on explainable, known behavior. A required mindset shift is: “Don’t just democratize data, democratize intelligence.”
The underlying goal of this approach is to reduce, and ultimately eliminate, the risk of hallucination in AI models. At scale, enterprises can afford to include human oversight and intervention only for exception scenarios, rather than for continuous 100% coverage. Hence, the need for deterministic guardrails to ensure AI agents don’t overreach.
Moving from Static to Active
The market is consolidating not just around “AI-ready” databases but also around systems capable of meeting the immediacy of the heavy transactional decisioning these new workloads require.
If your database is merely a “passive” record of what happened in the past, it will be the bottleneck for your AI’s future. The winners of the 2026 economy will be enterprises that turn their event data streams into immediate, deterministic actions, leveraging AI appropriately.
Key Takeaways
- Agentic AI requires deterministic guardrails for mission-critical workloads.
- Probabilistic AI models cannot replace ACID-compliant transactional systems.
- Real-time decisioning demands low-latency, streaming transactional architectures.
- Determinism-First architectures reduce hallucination risk in production AI systems.
- Enterprises must move from passive data storage to active, deterministic execution.
