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20/08/2026 6:05 pm
TL;DR: Building a shopping assistant shows that forcing every interaction through agentic reasoning adds latency, cost, and control risk when clear intents can be handled deterministically, while open-ended, multi-step tasks still need a ReAct-style loop, TruFoundry’s analysis finds. TrueFoundry’s analysis also shows that state must be organised around workflows and explicit context, not just conversation history, to keep multi-product assistants reliable.
NHIMG editorial — based on content published by TruFoundry: Deterministic vs Agentic Workflows: Lessons from Building a Shopping Assistant
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
Questions worth separating out
Q: How should teams decide when an AI assistant needs agentic reasoning?
A: Use agentic reasoning only when the task requires tool choice, intermediate evaluation, or multi-step decision-making.
Q: Why do explicit state boundaries matter in multi-step AI workflows?
A: Explicit state boundaries prevent stale context from leaking across tasks, products, or users.
Q: What breaks when AI assistants rely on flat conversation history?
A: A flat history accumulates irrelevant context, increases token load, and makes it hard to know which object the user means.
Practitioner guidance
- Define deterministic paths for routine tasks Map clear-intent actions such as specifications, reviews, and coupons to direct tool execution so the assistant skips unnecessary reasoning cycles and reduces latency.
- Reserve agentic reasoning for ambiguous workflows Use ReAct-style loops only when the task depends on intermediate findings, such as inventory resolution, store discovery, or fulfilment selection.
- Scope state by workflow and product identity Separate user-level preferences from product-level context, and refresh downstream variables when current_product_id changes to prevent stale references.
What's in the full article
TruFoundry's full blog post covers the implementation detail this analysis intentionally leaves at the architecture level:
- The exact ReAct step sequence used for inventory and purchase workflows, including planning, execution, and synthesis
- The workflow map showing how product, search, inventory, and purchase states are separated in practice
- The product reference resolution logic used when users say "this" or switch between multiple items
- The performance trade-offs between deterministic routing and agentic loops under different latency and token-load conditions
👉 Read TruFoundry's analysis of deterministic workflows and agentic reasoning in shopping assistants →
Deterministic workflows vs agentic reasoning: what teams need to know?
Explore further
20/08/2026 6:40 pm
Deterministic-first design is the right default for AI assistants. The article reinforces a principle NHIMG sees repeatedly in agentic systems: if a task is predictable, agentic reasoning only adds cost and risk. A deterministic path reduces the opportunity for tool confusion, unnecessary context growth, and accidental overreach. For identity programmes, that maps cleanly to least privilege at runtime. The practitioner conclusion is to reserve autonomy for ambiguous tasks and keep known requests on a narrow execution path.
A question worth separating out:
Q: How should organisations govern external tools used by AI agents?
A: Organisations should review external tools as security inputs, not convenience features. Each tool needs ownership, approval, metadata inspection, and ongoing monitoring for hidden instructions or unexpected behaviour. If an AI agent can act on a tool, then the tool’s provenance and control status should be governed like any other sensitive integration.
👉 Read our full editorial: Deterministic workflows still beat agentic reasoning for AI assistants
