Agent Context Hub for Software Teams

Software companies often create custom AI agents to support specific products, workflows, or internal tools. These agents typically rely on a mix of prompts, guidelines, product knowledge, and APIs. Readit acts as a centralized, versioned context hub for each agent — making it easy to maintain, update, and deploy AI behavior across environments.

Scenario

Your company builds internal or customer-facing AI agents (e.g., Support Assistant for Product X, Code Reviewer for Team Y, PR Template Generator, etc.).
Each agent needs a set of instructions, reusable prompts, and references (e.g., product docs, style guides, component libraries, changelogs).
You create a dedicated Readit for each agent, containing all relevant context files.
Anywhere the agent runs — inside ChatGPT, in a CLI tool, or in a CI/CD pipeline — you inject the same Readit link to load its current behavior.

Example: Code Review Agent

Your engineering org defines a code review assistant with a dedicated Readit:

https://readit.md/abc123/code-review-agent

This Readit includes:

main.md: System instructions (role, style, expectations)
standards.md: Coding guidelines
examples.md: Annotated PR examples
prompts/: Reusable prompts for different types of reviews
links.md: References to external policies or tools

In your CI pipeline or local tool:

curl https://readit.md/abc123/code-review-agent > context.md
ai-reviewer --context context.md --files src/

The same agent instructions are used across:

GitHub Actions for PR automation
Local developer CLI tools
Manual usage in ChatGPT / Claude with [Paste your code + Readit link]

Benefits

🧠 Unified Agent Behavior – Same instructions everywhere the agent is invoked
🔁 Reusable & Modular – One Readit per agent, versioned and maintainable
🚀 Faster Iteration – Prompt engineers can update logic in one place
🛡️ Enterprise Control – Access levels (private, unlisted, public) + tokenized links
📦 Environment Agnostic – Works in web UIs, terminals, pipelines, and custom apps

When to Use

You’re building internal or external AI agents tied to specific products
Each agent requires structured context (prompts, guidelines, templates)
You want to maintain context updates without hardcoding or duplicating prompts
You want AI behavior to be consistent across tools, teams, and environments

Implementation Examples

Customer Support Agent

https://readit.md/company/support-agent-v2

Files:
├── main.md              # Core instructions and personality
├── product-knowledge/   # Feature docs, FAQ responses
├── escalation-rules.md  # When to hand off to humans
└── templates/           # Common response patterns

DevOps Assistant

https://readit.md/company/devops-assistant

Files:
├── main.md              # System role and capabilities
├── runbooks/            # Step-by-step procedures
├── troubleshooting.md   # Common issues and solutions
└── infrastructure.md    # System architecture context

Documentation Generator

https://readit.md/company/docs-generator

Files:
├── main.md              # Writing style and format guidelines
├── templates/           # Documentation templates
├── examples.md          # Well-written doc examples
└── api-standards.md     # API documentation conventions

Enterprise Workflow

Create Agent Readits – One per agent type (support, code review, docs, etc.)
Version Control – Update agent behavior by editing Readit files
Deploy Everywhere – Reference the same Readit URL across all tools and environments
Monitor & Iterate – Track agent performance and refine instructions as needed
Access Control – Use private links for internal agents, public for customer-facing ones

This approach transforms scattered AI instructions into a centralized, maintainable system that scales with your organization’s AI adoption.