Delegation and agent workflows

OK, do that

Through Herculean’s MCP integration, a connected AI service can discover tools for reading and managing your board.

Once you’re sufficiently organised, Herculean can reach a point where delegating complex work to your AI agents becomes comically simple.

You can hand an agent a task ID, tell it to get on with the work, and let it execute on your behalf using the tools available to it.

This becomes particularly powerful when you’ve already configured Herculean with meaningful labels, detailed descriptions and useful Memories. These give your AI agents the context they need to understand your intentions, interpret how you’ve organised your work, remember important instructions and follow your preferred ways of working.

At this stage, tasks become more than instructions. They carry the context, requirements and expectations needed for an agent to work with considerable independence.

You can assign an agent a complicated task and simply say, “OK, do that.”

Your agent can then retrieve the relevant information from Herculean, carry out the work, document its findings and return the results for your review.

The goal is to spend less time explaining, organising and supervising, and more time deciding what you want to achieve.

Wider scope

Agents can also search for work and give you a better overview of your entire workload due to their ability to process all of your projects and tasks in Herculean faster and at scale than any human could.. You might ask a connected service to find tasks with a Codex label, look for Backlog tasks in a project, or search by other supported criteria. Labels can help identify a pool of work intended for a particular agent or workflow.

AI delegation isn’t limited to completing individual tasks. You can also put your agents to work organising and maintaining your entire productivity system.

For example, you can ask an AI agent to review every task within a project, or even across your entire Herculean board, and apply existing labels based on task descriptions and context. Your agent can also suggest new labels that would help you organise your work more effectively.

You can go further by asking your agents to identify relationships between tasks, recommend and establish dependencies, or estimate realistic deadlines based on available information and supporting evidence.

Once your work is recorded in Herculean, it becomes searchable, structured and accessible to your connected AI agents.

This means you no longer need to spend hours manually reviewing, categorising and reorganising everything yourself. Your agents can take on much of that administrative work, while you retain control over important decisions.

When connected to your AI, Herculean gives your AI agent all the capability to be a personal assistant, coach and project manager in one.

Dilligent agents & human-in-the-loop

AI agents can update task descriptions, document their work and move tasks through Herculean’s workflow as they progress.

A particularly effective example is delegating research tasks to AI agents.

Herculean has regularly been used for tasks where a human provides little more than a research topic and a few sentences of instruction. An AI agent then takes that initial request, performs the research and returns a comprehensive breakdown directly within the task description, complete with supporting sources, links, tables, comparisons and detailed findings.

Once the research is complete, the agent moves the task into the Review column, ready for the human to examine the results, make decisions or provide further instructions.

What started as a few sentences can become a substantial, documented piece of work with very little additional human involvement.

This is precisely where Herculean delivers its value.

Rather than confining AI agents to chat windows, temporary conversations and disconnected suggestions, Herculean provides a persistent, structured working environment where agents can organise their activities, preserve findings and deliver useful results directly into your productivity system.

It doesn’t eliminate the possibility of AI hallucinations or mistakes, but it gives you a clearer record of what an agent has done, what evidence supports its conclusions and what still needs human verification.

Research is an especially powerful use case. You can give an agent a subject to investigate, let it work through the available information and have it return with documented findings when your input or approval is required.

The objective is to move beyond AI that simply tells you what you could do, towards AI that actually does the work and brings you the results.

Learn how labels can route work and how dependencies can express task order.