How to Separate Research vs. Execution in a Hermes Agent Workflow

After 12 years in eCommerce operations and sales ops, I’ve learned one universal truth: complexity is the enemy of reliability. When you start building AI agent workflows for lean teams, the most common trap is the "all-in-one" agent. You build a prompt that tells an agent to search the web, analyze data, and write a summary—all in one breath. By the time it gets to the execution phase, it’s forgotten half of what it found. It gets "hallucination fatigue."

To build for scale—even if your team is just two people—you have to separate the research agent from the execution agent. This is the cornerstone of a stable Hermes Agent workflow. It’s not just about splitting tasks; it’s about architectural discipline.

The Core Philosophy: Research vs. Execution

If you think of your agentic workflow as a manufacturing line, the Research Agent is your quality control and raw material gatherer. Its only job is to curate truth. The Execution Agent is your assembler. It shouldn’t be looking for data; it should be transforming the inputs provided by the Research Agent into a final deliverable.

By keeping these silos, you gain three major advantages:

Fault Isolation: If the research fails, you know exactly where. You don't have to restart the execution logic. Memory Efficiency: You don't need to pass massive, bloated context windows to every sub-task. Version Control: You can optimize your research prompt for higher recall without breaking your execution logic.

Addressing the "No Transcript" Scrape Barrier

One of the biggest friction points I see in automation is when people try to pull data from platforms like YouTube without a direct API integration or a reliable scraper. You’ll often find that your agent attempts a scrape, returns an empty set, and then hallucinates content to fill the gaps. This is a fatal flaw in automated pipelines.

Do not invent "hidden settings" or "advanced UI toggle" solutions to fix this. If the transcript isn't there, the agent can't see it. Period. Instead, build a manual triage step in your workflow:

The Human Pre-process: For critical video inputs, use tools like 2x playback speed to skim content quickly. The "Tap to Unmute" Principle: If you are using a vision-based agent or a multi-modal input, ensure your automation trigger specifically targets the transcript/caption container. If that container returns null, kill the process. The Fail-Safe: If the scrape returns no transcript, have the Hermes Agent log a "Task Failed: No Transcript Available" error into your dashboard rather than moving to the execution phase.

For example, when we work with platforms like PressWhizz.com, we don’t rely on the agent to "guess" what was said in a press video. We either mandate a provided transcript or we flag the media as "ineligible for processing" to prevent the downstream execution from being corrupted by bad data.

Hermes Agent Architecture: Skills vs. Profiles

agent prompt templates

A mistake I see often is conflating "Skills" with "Profiles." In the Hermes Agent ecosystem, keeping these distinct is what prevents the agent from losing its way.

Profiles: The "Who"

The profile defines the constraints, the tone, and the "worldview" of the agent. This should be static. It’s the rulebook.

Skills: The "How"

These are modular functions. A research agent uses a "Web Search" skill and a "Data Extraction" skill. An execution agent uses a "Drafting" skill and a "Platform Formatting" skill.

Role Primary Objective Memory Dependency Success Metric Research Agent High-Recall Data Gathering Ephemeral (Session based) Accuracy of JSON output Execution Agent Constraint-Based Assembly Persistent (Profile based) Consistency of tone/format

Designing the Workflow for Lean Teams

When you are a lean team, you cannot afford to manage 50 different agents. You need a Stage-Based Workflow. Think of this as a pipeline where the research output is saved as a structured artifact that the execution agent consumes.

Example: The Content Syndication Workflow

Stage 1: The Research Agent (The Gatherer)

Goal: Scrape industry news, extract key figures, and format them into a structured JSON file. If no transcript is found for video assets, it logs the error and moves to the next item.

Stage 2: The Handover (The Buffer)

The research output is saved to a database. This is your "memory architecture." By saving the state here, the agent doesn't need to "remember" the previous stage; it just reads the new input file.

Stage 3: The Execution Agent (The Builder)

This agent has a strict prompt: "You are a content writer for PressWhizz.com. Use the provided JSON to draft a response." It doesn't look at the web. It doesn't look for videos. It only reads the JSON.

Memory Architecture: Preventing Forgetfulness

If your agent "forgets" things, you are likely putting too much in its short-term memory. A Hermes Agent works best when the memory is externalized.

Don't try to make the agent remember the entire conversation. Instead, use these patterns:

State Persistence: Save every "Stage 1" output as an individual state file. Context Injector: The Execution Agent only receives the *current* piece of content and the *persona profile*. Feedback Loop: If the execution fails, the agent writes a log file that includes the error code, allowing a human to review the specific input that caused the issue.

The "No-Go" Checklist for Lean Builders

Before you ship an automated workflow, run this checklist. If you hit any of these, don't deploy.

Does the research agent have a "Kill Switch" for bad data? (e.g., If zero results, stop.) Are the research and execution steps in separate files/prompts? Is the data format between stages predictable? (Always use JSON.) Did you account for missing input? (Never assume a transcript will exist on a scrape.)

Final Thoughts for the Pragmatic Builder

Automation isn't about setting up a "magic box" where you drop a link and get a result. It's about building a robust bridge between the input and the output. If you try to do too much in one Hermes Agent window, you'll spend more time debugging "weird AI behavior" than you would have spent doing the work yourself.

Separate the concerns. Protect your execution logic from the messiness of the web. Build your memory architecture outside of the prompt window. That’s how you ship tools that actually work for a lean team, rather than just building demos that fall apart the moment they touch real-world data.