Agents are coming. Most of the field isn't ready.
Agentic AI is not a different technology — it is the next generation of the tools you are already using. Here is what changes, and what it means for health communications.
Two generations of AI adoption — and most agencies are still in the first
There are two generations of AI adoption in health communications. Most agencies are in the first. Some are beginning the second.
Generation one is what almost everyone means when they say they are “using AI”:
• Open ChatGPT or Claude
• Type a prompt
• Get an output
• Review it, fix it, decide what to do next
• Repeat
This works. It saves time. It is genuinely AI. The outputs are real, the efficiency gains are real, and the agencies doing it well have built something valuable.
But in this model, you are still in the loop for every single decision. You set the goal, plan the steps, review the output, decide what comes next, and start again. The AI handles one step at a time. You handle everything else.
The person doing the reviewing, the sequencing, the fixing — they are functioning as the system’s prefrontal cortex. That work is invisible on a timesheet, but it is skilled labour. It is currently being performed by people paid to write, think, and advise.
Agentic AI is the second generation — and it changes that fundamental dynamic.
Instead of responding to prompts, an agent receives a goal and gets on with it. It plans the steps itself. It uses tools. It checks its own work. It reports back when it is finished — or when it genuinely needs a human decision.
The shift is not from “not AI” to “real AI.” It is from AI as a responsive tool to AI as an autonomous system. Generation one and generation two both matter. But they require different things from the people working with them.
What makes an agent different
An agent receives a goal, not a task. Then it gets on with it.
What generation two does that generation one doesn’t:
• Works out the steps itself — no prompt-by-prompt hand-holding
• Uses tools: searches the web, reads documents, writes to files, calls external systems
• Checks its own outputs and adapts when something fails or produces an unexpected result
• Reports back when it has finished — or when it genuinely needs a human decision
The difference isn’t technical. It’s about where the decision-making sits.
A prompt-and-response workflow:
• Human decides what to do
• Human writes the instruction
• AI executes one step
• Human reviews
• Human decides what to do next
• (Repeat indefinitely)
An agentic workflow:
• Human defines the goal and the guardrails
• Agent plans and executes the steps
• Agent flags genuine decision points
• Human reviews outputs and exceptions
• Agent handles the rest
Gartner predicts 40% of enterprise applications will include task-specific AI agents by end of 2026 — up from under 5% in 2025. The transition is already underway.
Who builds agents, and how
Agents are not a product you buy off the shelf. They are assembled from components — and understanding what those components are matters if you want to evaluate vendor claims or build anything yourself.
The foundation models — the brains of any agent:
• Claude (Sonnet, Opus, Haiku) by Anthropic — strong reasoning, long context, tool use
• GPT-4o / o3 by OpenAI — largest ecosystem, widest third-party support
• Gemini by Google DeepMind — deep integration with Google Workspace
• Open-source models (Llama, Mistral) — self-hostable; useful where data cannot leave your environment
The frameworks — the scaffolding that turns a model into an agent:
• LangGraph — the most widely used in production; models workflows as directed graphs
• CrewAI — role-based multi-agent crews; a working system in under 20 lines of code
• Claude Agent SDK by Anthropic — tool-use-first architecture; powers Claude Code
• OpenAI Agents SDK — released March 2025; production-grade, handoff-based architecture
All of these require a developer to set up. But the landscape is changing.
Can we build our own?
Yes — and the barrier is lower than most people assume.
Three realistic entry points:
1. No-code agent builders — tools like n8n, Zapier AI Agents, and MindStudio let you connect models to workflows without writing code. You describe what the agent should do; the platform handles the wiring. Suitable for: literature surveillance alerts, first-pass claims checking, content routing.
2. Low-code platforms with a health focus — Infinitus Studio launched in April 2026 as the first healthcare-specific no-code agent builder. Agents built on it are reportedly 40% more accurate and deployed up to 90% faster than manually developed systems.
3. Custom-built with developer support — a small agency with a developer or good technical partner can build purpose-specific agents using LangGraph or CrewAI in weeks, not months. The AI agent market hit $7.84 billion in 2025 and is on track for $52.62 billion by 2030. The ecosystem of specialist builders is growing fast.
What to watch for if you go this route:
• Compliance first — any agent handling unpublished data, patient-adjacent content, or regulatory submissions needs proper data governance. Most consumer no-code tools are not compliant by default.
• Start narrow — one structured workflow, not a general-purpose assistant. Claims checking before MLR preparation. Surveillance before content generation.
• Audit trails matter — for health communications specifically, you need to be able to show what the agent did and why. Choose tools that log decisions, not just outputs.
Why health communications is unusually well-positioned for this
Not every industry is ready for agentic AI at the same time. Health communications is, for one specific reason: the workflows are already structured.
The workflows that are ready to hand over:
• Claims checking — structured, verifiable, rule-governed; right or wrong
• Reference verification — binary correctness against a known source
• Literature surveillance — repeatable search and filter logic
• MLR preparation — document-heavy, defined inputs and outputs
• Content adaptation — clear rules for what counts as correct across formats
The MLR market is valued at $13.1 billion in 2025, projected to reach $27.1 billion by 2032. Early AI pilots show 50–65% reductions in regulatory submission timelines. Some companies currently run MLR review cycles of 50–60 days per piece of content. Agents compress that.
These are not creative problems. They are structured processes that currently require skilled labour partly because the tools to automate them safely did not exist.
Those tools are starting to exist now.
What will not be automated
An agent can verify that a claim is accurate. It cannot decide whether that claim is appropriate to make.
Those are different problems.
The judgements that stay with humans:
• Contextual authority — understanding the regulatory, commercial, and reputational environment in which a communication will land
• Scientific integrity — deciding how much uncertainty a physician can usefully hold when making a prescribing decision
• Audience reading — knowing what a room needs to hear, and what it does not
• Accountability — being the person whose name is on the submission, the advisory board output, the label claim
“AI will not replace MLR, but it will transform it into a faster, safer, and more strategic safeguard.”
Source: Pharmaphorum — Accelerating the MLR review leveraging AI
None of these can be broken into verifiable steps and handed to a system. They require professional judgement — and that judgement becomes more visible, not less, once the surrounding process work is handled.
The question that matters right now
The question is not whether your agency will use agents. It will.
The question is whether the people leading it understand enough about what agents can and cannot do to design the handoffs correctly.
What that means in practice:
• Knowing which workflows are structured enough to run autonomously
• Knowing where human review is a genuine quality check — and where it is just a comfort habit
• Understanding what regulatory accountability requires in terms of human sign-off
• Knowing how to brief an agent so its outputs are auditable, not just plausible
Most agencies are not asking these questions yet. That is not a criticism — the technology has moved faster than the governance frameworks. But the gap will close, and the organisations that close it intentionally will be in a different position from those that close it by accident.
This is the first post in a focus on agentic AI in health communications — covering what agents are, how to build them, and what the commercial landscape looks like. If you are working on this, or thinking about it, follow along. There will be a new section devoted to agentic solutions.
— Ned
#IrreplaceablesHealth
Sources
Gartner via DEV.to — 2025 was about chatbots, 2026 is about agents
GlobeNewswire — MLR Review Software Market Report 2026
Pharmaphorum — Accelerating the MLR review leveraging AI (Part 1)
Anthropic — Building Effective Agents
LangGraph — Best Multi-Agent Frameworks 2026
PEC Collective — AI Agent Frameworks Compared