Most Agentforce Actions are built to talk to people. That works fine in a 1:1 chat, but the moment another agent, a Data 360 pipeline, or automated process needs to use them, the system breaks down. The difference comes down to one fundamental design choice: is your Action returning prose or is it returning structured data?
An Action returning a formatted paragraph is optimized for a human reader. An Action returning clean, structured data is optimized for an entire ecosystem that needs to act on it. Those are fundamentally different designs — and most teams are defaulting to the wrong one without realizing it.
The Problem with Answers
When you design an Action purely for conversation, you naturally bundle everything together — raw results, context, interpretation, a recommendation. The Action does the thinking so the human doesn’t have to.
Consider an Action built to generate a Data Extension segment in Marketing Cloud Engagement (MCE). Designed for a human, the output looks like this:
✅ Segment created successfully!
Your new Data Extension “ReEngage_Q3” is ready.
- 14,203 subscribers added
- Unsubscribes excluded
- Recent sends (30 days) filtered out
You can find it in Data Extensions > Campaigns.
Next step: head to Email Studio to build your send!
Readable? Absolutely. Useful? For sure. Usable by the next Agent in a workflow? Completely useless. When you force an Agent to parse unstructured text instead of clean data, you are relying on an LLM’s interpretation instead of deterministic logic, which simply means it has to guess. That causes unnecessary delays and makes your automated workflows far more likely to break.
The Agentic-Friendly Alternative
Now consider the same execution designed for an agent to act on:
{ "task": { "id": "seg-2026-0707-114", "intent": "Isolate customers inactive 90+ days for re-engagement campaign", "initiated_by": "agent" }, "result": { "status": "success", "summary": "Created Data Extension 'ReEngage_Q3' with 14,203 subscribers from Master_Subscribers, excluding unsubscribes and 30-day recent sends", "data": { "de_name": "ReEngage_Q3", "de_key": "reengage_q3_2026", "row_count": 14203 } }, "state": { "current": "Segment built, not yet targeted by any send", "changed": [ "New DE created in Data Extensions > Campaigns folder" ] }, "next": { "recommended_action": "Build email send targeting this DE", "required_context": [ "Approved re-engagement template", "Send window rules" ], "constraints": [ "Suppress anyone contacted in last 30 days", "Frequency cap: 2 sends/week max" ] }, "meta": { "timestamp": "2026-07-07T11:42:00Z", "source_tool": "sql_playground_segment_builder", "confidence": "high" } }
Same underlying capability. Completely different architectural value.
While a human might see a wall of code here, an Agentforce orchestration layer sees context, constraints, and actionable parameters. This payload gives the LLM exactly what it needs to seamlessly trigger the next automated Flow or turn around and generate a beautifully conversational summary for the user in the chat UI.
This structured version answers the five core questions an orchestrating agent actually needs without forcing it to guess:
- Why? — the intent behind the action
- What happened? — the outcome and status
- Where do things stand? — the updated system state and applied filters
- What’s next? — the recommended action with explicit keys and requirements
- What are the rules? — the hard constraints (like frequency caps)
A human doesn’t need all of that spelled out in a conversational chat. An agent can’t function without it.
Why it Matters Now
AI agents don’t just read information— they look for specific facts to then take action. When an Action returns a paragraph of text, the agent has to interpret what it’s reading before it can decide what to do next. That extra step introduces ambiguity and failure points that could compound across every step in a Flow or agent workflow.
Structured output solves this problem. It’s unambiguous, composable, and works whether the caller is a human in an Einstein Copilot conversation, an Agentforce agent mid-workflow, or a scheduled Flow running overnight.
The Practical Fix
This isn’t a major rebuild. It’s just a design decision you make when you’re setting up your Action outputs — whether that’s an invocable Apex method, a Flow’s output variables, or an external service Action schema.
When you create an Action, ask one question: who is using this output?
If the answer is a human, conversational text works fine. If the answer is another Action, an agent, or an automated process — return structured data. Make the output fields explicit, keep them clean, and let the caller decide what to do with them. An Action designed for data works across your entire toolkit. An Action designed for conversation only works in a chat window.
Where to Start
The future of enterprise AI isn’t only about having a great conversation in a chat window; it’s about seamless orchestration behind the scenes. By shifting your default output from friendly text to clean data, you make sure your Agentforce setup is ready to scale across all of your Salesforce tools and custom systems.
About the Author
Cameron Houser
Academy Enablement Lead
Enablement Strategist | Salesforce Expert | AI Enthusiast
With nine years of consulting experience across the Salesforce ecosystem, Cameron empowers individuals to think beyond standard features and solve complex business challenges. As the Academy Enablement Lead at ListEngage, he is driven by a clear mission: making consultant-level mastery the baseline, not the finish line. An avid advocate for the future of work, Cameron also explores how AI, automation, and next-generation tools can accelerate learning and optimize performance
ListEngage Editorial Team
Fred Homner
Director of Customer Success & Salesforce Practices
Sam Kosakowski
Solution Architect
Zanah Kagan
Marketing Cloud Practice Lead