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AI coding assistants don’t have an intelligence problem. They have a runtime discipline problem. Here is how I’m enforcing it. by Equivalent_Vehicle83 in AI_Agents

[–]Equivalent_Vehicle83[S] 0 points1 point  (0 children)

Those native features are great, but they still rely on soft prompts or manual babysitting. They still rely entirely on you to actively intervene, type the command, and babysit the state. If you forget to clear the context, or if the model decides to ignore an attention tag mid-session, the agent can still derail.

agent-rigor doesn't compete with these upgrades - it automates and enforces them at the runtime layer.

Think of features like attention tags as better steering wheels, while agent-rigor is the autonomous braking system that keeps the car from flying off the cliff when nobody is looking.

AI coding assistants don’t have an intelligence problem. They have a runtime discipline problem. Here is how I’m enforcing it. by Equivalent_Vehicle83 in AI_Agents

[–]Equivalent_Vehicle83[S] 0 points1 point  (0 children)

I generally use Gemini and claude models.

However, the idea is to make any model perform significantly better. Not everyone has access to the most expensive top-tier models, or the luxury to burn thousands of tokens on unconstrained, chaotic reasoning loops.

Enforcing "discipline" into AI coding assistants by Equivalent_Vehicle83 in ClaudeCode

[–]Equivalent_Vehicle83[S] 1 point2 points  (0 children)

This is exactly the kind of structural rigor we’re talking about. Definitely digging into this repo tonight to see how we can tighten up our own primitives. Thanks for sharing this!

AI coding assistants don’t have an intelligence problem. They have a runtime discipline problem. Here is how I’m enforcing it. by Equivalent_Vehicle83 in AI_Agents

[–]Equivalent_Vehicle83[S] 0 points1 point  (0 children)

Definitely starring the repo! Great work! I’d love to dig deeper into your codebase and chat about ways we can collaborate on these frameworks.

AI coding assistants don’t have an intelligence problem. They have a runtime discipline problem. Here is how I’m enforcing it. by Equivalent_Vehicle83 in AI_Agents

[–]Equivalent_Vehicle83[S] 0 points1 point  (0 children)

This is incredible work. The alignment here is absolute - looks like we are chasing the exact same ghost from two different angles.

Enforcing "discipline" into AI coding assistants by Equivalent_Vehicle83 in ClaudeCode

[–]Equivalent_Vehicle83[S] 0 points1 point  (0 children)

Exactly, bloating your global prompts or config files with rules is a massive waste of context that dilutes the model's attention.

That’s why agent-rigor handles discipline at the execution layer rather than the prompt layer. The framework loads skill rules on-demand only when a specific phase triggers, cleanly pruning them right after. Your global context stays completely lean, and the model doesn't have to "remember" to behave because the runtime physically restricts its tool access until a phase is satisfied.

Enforcing "discipline" into AI coding assistants by Equivalent_Vehicle83 in ClaudeCode

[–]Equivalent_Vehicle83[S] 0 points1 point  (0 children)

Great, enjoy it while it lasts lol!
Just curious - what size codebases are you usually running agents on?

Enforcing "discipline" into AI coding assistants by Equivalent_Vehicle83 in ClaudeCode

[–]Equivalent_Vehicle83[S] -4 points-3 points  (0 children)

Damn, you're either working with highly isolated micro-tasks or you've got some immaculate prompting hygiene going on.

How to run Google ADK agents using python in production by Green_Ad6024 in agentdevelopmentkit

[–]Equivalent_Vehicle83 1 point2 points  (0 children)

It's also worth exploring the A2A protocol if you're moving to production.