Structuring AI Agent Tasks: A Repository Pattern for Systems Administration & Dev-Ops

As AI agents become increasingly capable of handling complex systems administration and DevOps tasks, the need for structured task definition has become apparent.

Today, I'm sharing this repository template I created for structuring very detailed task definitions for agents.

Its objective is to bring rigor and precision to task-led prompting for AI agents by trying to provide not only context and mechanisms for logging, but other elements needed throughout the typical lifecycle of using an AI agent for a task. Think things like:

Secrets handling: Defining the mechanism(s) for using secrets for this particular task. This could be as simple as "use the dot env in the repo" to "use this secrets manager with this CLI".
Success definition: Tightly circumscribing complex and multi-step tasks into chunks means that it becomes important that the AI agent know when it's time to stop so that the next step can be initiated without what may be a quietly spiraling context trail that is turns away from rendering future inference almost useless (I call this phenomenon long context mud). Another challenge (excluding systems with dedicated human in the loop) is AI agents falsely proclaiming that the task has been a glowing success. This structure aims to reduce the tendency of agents to make those optimistic but often woefully wrong proclamations by defining when it's okay to say that the task is finished.

The Challenge: Context Overload and Task Sprawl

Anyone who has worked extensively with AI coding assistants over the past year has likely encountered what I call "long context mud" - that moment when an AI agent becomes overwhelmed by too much context and starts producing degraded output.

As this is such a pronounced drawback of agentic AI tools, various approaches have come to market over the past six months to try to mitigate against this challenge. One approach that is growing in popularity is for tools to generate planning documents which provide a task list for the agent to work through. This has also been encapsulated in MCP servers like Anthropic's sequential thinking.

The approach is solid, although in practice results are often mixed. However, in addition to relying on this emergent tooling, users may wish to develop a proactive approach that aims to provide that task sequence before the agent even gets introduced to the repository.

Task Modularization Through Repository Structure

Breaking down multi-step projects into chunk-sized bites doesn't only help humans - it helps AI agents too. Generation: Flux Dev (Via Leonardo AI)

The core principle which underpins the success of this methodology is modularisation and chunking.

Not only does this apply to how one provides context to AI tools (or how one provides audio to a real-time speech to text API for instance!). It also applies to how one defines and provides a task definition to those same AI agents.

Just as an AI agent will begin to struggle when flooded with context from a huge code repository, if you attempt to tell it everything about your aspirations for this project at once, how you want it to manage secrets, what the success criteria are, it might similarly struggle to take it all in in an effective and meaningful way.

The task chunking, like most things in AI, can be approached and done from different directions.

On the one hand, you can simply fill up a template repository like the one I'm sharing.

On the other, you could do something like provide an agent with the unorganized bank of data and have it divided into parts. Of course, this is counterintuitive in a sense as you're asking that first agent to do what you otherwise would have done to the 'executor'. But like pre-processing data before injecting it into a RAG database (which entails, essentially, the same idea), in practice, this often bears results. The reason is ... again ... chunking. By dividing the work into task preprocessing and task execution you're dividing and conquering, providing very narrowly defined agents with micro-tasks within the context of the overall project execution.

Repository Structure

The template follows a consistent structure that provides comprehensive task definition. But of course this is not prescriptive. It can also be significantly expanded upon. If your AI workflows involved handling sensitive information, you may want to define a guardrails file to provide the agent with information about the guardrails framework which will be governing its overall behavior. Similarly if the agent will be operating within a sandboxed environment for system security, you might wish to provide a file like sandbox.md giving the agent context on the constraints facing it in this environment. Deployments.md can outline the deployment and CI/CD pipeline (etc).

But a basic model could look something very like:

text

1task-repository/
2├── tasks/
3│   ├── project-outline.md    # High-level overview and requirements
4│   ├── details.md           # Technical specifications and configurations
5│   ├── remote.md            # Target environment details
6│   ├── secrets.md           # Secure credential access instructions
7│   ├── mcp.md              # Required MCP server configurations
8│   └── success.md          # Measurable completion criteria
9├── logs/
10│   └── progress/           # Agent progress tracking
11└── README.md              # Task summary and instructions

Key Components

Project Outline serves as the foundation document, providing comprehensive understanding of what needs to be accomplished. This includes functional requirements, architectural considerations, and user interface specifications.
Details contains the technical nitty-gritty: specific configurations, command sequences, file locations, and implementation specifics that the agent will need during execution.
Remote Environment specifications are crucial for systems administration tasks. This document outlines target server details, IP addresses, access methods, and environment-specific constraints.
Secrets Management provides secure guidance on accessing necessary credentials. Rather than storing secrets (which would be unsafe), this document instructs the agent on which tools to use - whether that's reading from `.env` files or interacting with formal secrets management systems.
MCP Configuration defines the Model Context Protocol servers required for the task. MCP servers extend AI agent capabilities by providing access to specialized functions and external services.
Success Criteria establishes measurable, specific completion criteria. This prevents scope creep and provides clear validation points for both the AI agent and human supervisor.

Suggested MCPs

As powerful as MCPs are, they also provide agents with what can be an overwhelming box of tools, which (perversely) risks flooding them with yet more context in the form of JSON usage definitions. For that reason, defining the allowed list of servers on a per-project basis is increasingly being used as a best practice workflow. Generation: Leonardo.ai (Flux Dev).

MCP servers are useful when engaging in system operations of the nature described here.

Although MCP is relatively early stage and not without its critics (Hacker News is a frequent place where that gets sounded out), even using a server just to streamline SSH commands (like MCP SSH) can quickly allow agents to do what they couldn't in the comparable environment of using something like Remote Explorer to expose them to an entire remote file system.

The MCP file is a drop-in replacement of sorts for including this information in a project rules file. You can use it to really flesh out not only which MCPs are running, but how to use them - and what to do when multiple MCPs can get the same job done (in other words, providing the agent with a simplified decision-making algorithm to folllow).

Foundational chunks can include directions like:

### Verification Steps 1. **Check SSH connectivity**: Verify `mcp-ssh` can connect to the target server2. **Test permissions**: Ensure adequate privileges for software installation and configuration3. **Validate network access**: Confirm connectivity to camera RTSP feeds from target server

The following MCPs are great to have in your toolkit (or more precisely, in your JSON!) if you frequently or increasingly find yourself turning to the LLM-plus-MCP pattern in order to get things done on computer systems, whether they be your local environment or on a remote.

MCPs For Filesystem Use, Docker Awareness

The following MCPs,, among the list of many options available, are particularly useful for using code generation agents for this kind of workflow:

SSH MCP Server: Essential for remote server access and command execution
Docker MCP: Container management and deployment operations
CodeMagic MCP: CI/CD pipeline management and automation

Cloud Infrastructure

AWS MCP: Amazon Web Services infrastructure management
Terraform MCP: Infrastructure as Code provisioning
DigitalOcean MCP: Droplet and service management
GCP MCP: Google Cloud Platform resource management

Real-World Application

A bot operating an NVR camera. Image: Flux Dev / Leaonrado.ai

Consider a typical scenario: deploying a Network Video Recorder (NVR) system across multiple remote servers. Traditional approaches might involve lengthy chat sessions with an AI agent, gradually building context and hoping the agent maintains focus throughout the multi-step process.

With the repository pattern, you would:

1. Define the task** in `project-outline.md` with specific requirements for camera integration, fault tolerance, and monitoring 2. Specify technical details** in `details.md` including RTSP configurations, storage requirements, and network settings 3. Document the target environment** in `remote.md` with server specifications and access methods 4. Outline credential access** in `secrets.md` without exposing sensitive information 5. **Configure required MCP servers** in `mcp.md` for SSH access and container management 6. **Establish success criteria** in `success.md` with measurable completion metrics

The AI agent then has everything needed to execute the task efficiently, with clear boundaries and success criteria.

Workflow Model

The basic workflow consists of two phases.

First the task is carefully outlined using the skeleton modeled above.

In organizational settings this task may then go through an approval layer.

Finally, when the specification is ready and version controlled, the AI agent is directly exposed to it for operation:

Benefits in Practice

This method of creating structured task repositories for AI agents, at least for major tasks, provides several benefits:

Task pooling: Organizations will increasingly find that they use the same agent for similar tasks. As AI agents continue to progress in capability and quality, organizations will begin to benefit from consolidating these task definitions in a central library. Keeping a copy of the inputs to AI enables more than streamlining the creation of prompt libraries. It creates a vault of information that can be used to build institutional knowledge for how best to configure AI agents for organization specific workflows.
Reduced frustration: While it's more fluid to define your task and provide context ad hoc as the agent operates in practice, this often needs to degrade that inference and an overall frustrating experience. My experience with having devised many task repositories following this model is that the performance is more predictable and reliable.
Reduce API costs: When AI agents go on and begin doing things like destroying useful code, the effects are more than just frustration and the immediate consequences. APIs bill for model use unselectively. Whether an agent fulfills its task on the first go or fails after hours of operation makes no difference to the API bill you will receive. Speccing out tasks in a robust and modular fashion reduces these failure loops that are both frustrating for humans and costly for businesses.
Handoffs and multiple model usage: With the preponderance of models and tools coming to market many people developing with AI including me are finding that the best approach is often trying out what's available and seeing what works for the task. Eventually model development will probably reach a more sustainable level and a clearer picture of the best tools and models for different workflows will hopefully formulate. But in the meantime, it's in the interest of many to keep projects relatively AI stack agnostic. Decoupling the project instructions from the execution is one way to do that