# How We're Thinking About Agentic Systems

DevFeed: [How We're Thinking About Agentic Systems](<https://devfeed.tech/articles/how-we-re-thinking-about-agentic-systems-20031.md>)

Original publisher: [Read original article](<https://technology.doximity.com/articles/how-we-re-thinking-about-agentic-systems>)

Author: Doximity

Published: 2026-06-15T10:03:00Z

Content type: opinion

Language: en

Sources: [Doximity](<https://devfeed.tech/sources/doximity.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Automation](<https://devfeed.tech/topics/automation.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [Authorization](<https://devfeed.tech/topics/authorization.md>), [observability](<https://devfeed.tech/topics/observability.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agentic-ai](<https://devfeed.tech/tags/agentic-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [automation](<https://devfeed.tech/tags/automation.md>), [observability](<https://devfeed.tech/tags/observability.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [systems](<https://devfeed.tech/tags/systems.md>)

## AI overview

Doximity discusses how to decide whether a workflow needs an agent, arguing that predictable processes are better served by deterministic automation. Agents are more useful when workflows require judgment, adaptation, tool selection, or interpretation of messy context, but they introduce additional failure modes and operational requirements.

## Source excerpt

In the current rush to adopt agentic systems, the most dangerous question isn't "what can we make agentic?" but "where do agents actually help?" At Doximity, we've found that moving from a working demo to a production-grade agent requires a shift in focus--from model capability to system design. That framing matters because a working demo and a production system are very different things. The demo proves something can work once. The product has to work repeatedly, with real users, real permissions, real data, and real failure modes. That matters even more in healthcare-oriented products like Ask, where information surfaced to clinicians helps support patient care. A few patterns are starting to feel useful: Solve the Workflow, Not the AI A valuable attribute of software engineering is knowing when not to overbuild. That sounds obvious, but it gets harder when the shiny new toy is genuinely useful. Agentic AI is one of those moments. Because agents can reason, call tools, and move through workflows in ways traditional automation cannot, it becomes tempting to look at every process and ask, "Could this be an agent?" That is the wrong first question. The better question is: what does this workflow actually need? If the process is predictable, repeatable, and follows a known set of rules, it probably does not need an agent. It needs good automation, clean inputs, reliable APIs, and clear failure handling. Agents become more useful when the workflow requires judgment: interpreting messy context, choosing between tools, adapting based on what it finds, or making decisions where the path is not fully known upfront. That distinction matters because adding an agent is not free. It introduces new failure modes around reasoning, permissions, observability, evaluation, retries, and edge cases. Sometimes that tradeoff is worth it. Sometimes it is just a more complicated way to do what a well-designed workflow could have handled deterministically. So the starting point should not