Where AI agents are delivering value in operations teams today, where simple workflows still win, and what it takes to run agents reliably in production.
Why this report
Most conversations about agentic AI start with the model. In practice the harder questions are about scope, tooling, evaluation and ownership. This report draws on Neura Dynamics delivery work across support, finance and e-commerce operations to answer them.
What it covers
We compare single-agent, multi-agent and deterministic workflow designs against the same set of operational tasks, and document the trade-offs in accuracy, latency, cost and maintenance effort. Each architecture is shown as a diagram you can adapt.
Who it is for
Operations and product leaders deciding where to invest next, and engineering teams planning their first agent in production.
Agent vs. workflow: a decision framework
Use a workflow when the steps are known in advance and an agent when the path depends on what the model finds. Score each task on variability, tool count and cost of error before choosing.
Reference architectures we deploy most often
Three patterns cover most deployments: a single agent with a small toolset, a router that hands off to specialist agents, and a deterministic workflow with one agentic step. Each is shown with its data flow and failure points.
Failure modes seen in production and how teams mitigate them
The most common failures are tool-call loops, stale context and silent partial completion. Teams that log every tool call and set hard step limits catch these early.
Cost and latency ranges by architecture
Single-agent designs are cheapest to run. Multi-agent systems add latency with each hand-off, so reserve them for tasks where specialisation clearly improves accuracy.