u/Lower-Impression-121

To "learn" RAG I (actually the IDE) under my (mostly its) instruction built a very powerful (according to the IDE) RAG system for very complex (they are, wargames rules are very complex and fought over - heaps of test cases for the eval uh, integration test suite) and technical documents.

It worked.

And got me thinking what to build next beyond the AI version of Hello World (which RAG is). Other than document or data analysis, what else is frameworkable, repeatable and should be a public repo instead of some startup idea because every org is going to have to do it and have one.

Just as the light bulb was wargames rules to RAG, the Game itself was a slap to the face. I have the Rules, I have the Game state - uh - Sim-u-lati-on (ppap reference there in case you missed it).

The Goal: To, using an underpowered laptop, no GPU, small Open Weight models, and a subscription plan to Antigravity (the self-harm is amazing), build (actually the IDE) a generic Simulation platform based on the rules in the aformentioned Rules RAG and custom game state (ie the map, units involved etc.) and have it optimise a turn, and play play adversarily against each other (against other Red Team Simulator).

It will be called Prime Radiant.

The Rules Set (Renegade Legion, which layers from Solar System Campaign to an RPG) is loaded.

And the end of it, I believe that if successful, the engine, which will also be shoved up onto github like the RAG, can be used for business simulation purposes such as the impact on sales if a product price was reduced, or what a competitors actions could be in response etc and many many other scenarios. (Its up to you, Dear Reader, to then provide your own Rules and "Game" state); and prove that OSS and LLMs can replace massively expensive proprietary systems.

After a conversation on whether Advanced Squad Leader (all versions) or Star Fleet Battles (plus Captains Log) was the better or harder end game, I have settled up Up Front! small enough, same rules format, some supplements and errata to start with and then progress ASL to SFB.

The objective is to have it answer accurately across versions and conflicts and overrides or special situations. If it can do that then we have an answer to the Rules Lawyer.

The activity will take a few days. Finding all the material will be the hardest part - boardgamegeek and wargamevault are first points of call. Assistance here would be greatly appreciated, I do this for the architecture not the content. My office wall of bookcase games attest.

After a conversation on whether Advanced Squad Leader (all versions) or Star Fleet Battles (plus Captains Log) was the better or harder end game, I have settled up Up Front! small enough, same rules format, some supplements and errata to start with and then progress ASL to SFB.

The objective is to have it answer accurately across versions and conflicts and overrides or special situations. If it can do that then we have an answer to the Rules Lawyer.

The activity will take a few days. Finding all the material will be the hardest part - boardgamegeek and wargamevault are first points of call. Assistance here would be greatly appreciated, I do this for the architecture not the content. My office wall of bookcase games attest.

https://preview.redd.it/aldj6vztcy8h1.jpg?width=1024&format=pjpg&auto=webp&s=7502333be3fa48fda7d432bdc9321700bae38a8c

Image and top-right Target State thesis expanded by gemini, collaborated with and refined by grok and chatgpt.

Quadrant Breakdown

• Top Right (Autonomous Enterprise Systems): Platforms that bridge high-reasoning autonomy with rigorous, process-centric governance. These systems prioritize organizational integrity, audit trails, and "Agentic JIT" execution over simple task-completion.
• Top Left (Autonomous Agent Frameworks): Code-first environments providing maximum flexibility for building complex agentic loops and state machines. They offer high reasoning power but require significant manual effort to graft on enterprise-grade security and process management.
• Bottom Right (Managed Work Platforms): Ecosystems that provide deep enterprise integration and stability. They excel in structured, deterministic automation but are historically rigid and limited in handling non-deterministic "reasoning" at scale. 
• Bottom Left (Workflow Automation): Platforms built for visual, deterministic integration. They are the standard for low-complexity, rule-based piping, serving as the connective tissue for data rather than the "brain" of the operation.

Detailed Tool Comparison

1. AWS AgentCore & Google Vertex AI (Autonomous Enterprise Platforms)

• Focus: Infrastructure-native governance and enterprise-scale orchestration.
• Strengths: Unmatched integration with cloud security, IAM policies, and VPC-level isolation. They leverage sophisticated "Agent-to-Agent" (A2A) protocols and Model Armor, providing the foundation for highly secure, multi-agent operations.
• Weaknesses: They remain "How-focused"—optimizing agent execution rather than the high-level business process. Achieving "Organizational Operational Correctness" still requires custom architecting above these layers.

2. LangGraph & CrewAI (Autonomous Developer Frameworks)

• Focus: Granular control over execution logic and state management through code.
• Strengths: Allows developers to craft intricate, multi-agent collaboration patterns that proprietary platforms often hide. Ideal for building custom AI logic that needs to be portable and highly modular.
• Weaknesses: They are raw engines, not turn-key platforms. They lack native support for enterprise audit logging, formal business process management (BPM), and pre-built Human-in-the-Loop (HITL) compliance dashboards.

3. Microsoft Azure AI Foundry / Copilot (Managed Work Platforms)

• Focus: Providing an enterprise-ready environment for AI that feels familiar to current organizational workflows.
• Current State: Deeply embedded in the M365 and GitHub ecosystem, moving from assistive "copilot" behavior toward more autonomous, background-task orchestration.
• Expected Trajectory: Aggressively moving to fill the gap between individual productivity and enterprise process control by leveraging their deep-seated footprint in existing corporate backends.

4. Claude Team / Cowork (Autonomous Enterprise Systems)

• Focus: Leveraging superior reasoning and "computer use" capabilities to handle complex, unstructured workflows.
• Current State: Leads in pure agentic reasoning and context analysis. It operates as a highly intelligent team member, though it currently sits "on top" of business systems rather than fully integrating into the legacy, process-heavy backends of a traditional enterprise.
• Expected Trajectory: Evolving from a "smart workspace" into a formal orchestrator by expanding its API surface area and integrating more tightly with enterprise protocol standards.

Summary

The market is converging. While frameworks like LangGraph and CrewAI provide the core logic engines, the "Top Right" is being contested by two different strategies: Cloud Providers (AWS/Google) are building outward from security and infrastructure, while Agentic Innovators (Claude/Expert-Proxy.com) are building inward from reasoning and process-centricity.

me: hmmm. are song lyrics too.... hidden meaning driven to be useful as prompts?
gemini: I cannot fulfill this request. (x2)

back context: i was mentioning how listening to darkwave helped (moved over from lofi) the thought processes. gemini was going on about how the genre meta matched the architetural style (as these things do to make you feel good about yourself) which lead to a short back and forth about communication quality being the role nowadays between Fingers and Circuits, and as music was on the mind I asked the above and it couldn't handle it.

Maybe AI's are zentraedi.

Just in time middleware. Agents writing the middleware code - in production - when they need to. All the error handling, the EIP pattern support, performance, schema changes. Get an endpoint, a payload of some form, maybe a spec, the agent takes care of what was a project or a change request with an SDLC and... internalises it all.

Not decision agents, those are prompting these coding agents.

What could go wrong? Lots, probably, yet no more than the loop of last year. Its faster to do it. Its as reliable. Its quicker at remediating itself.

Middleware - gone (over time). Decision agents replace business rules/process engines (over time) - gone.

Left: CRUD state stores - even the saas. The "harness" (the organisational agentic OS), and it is only a combination of the two as a control/obersation layer.

Decisions agents, they are baked from the SMEs.

Sure sometimes I can ask a question that isn't clear enough - yet "what impact do you think this role (AI process doer and imho whoever gets will be a least liked person) will have on your organisation?" is pretty clear given whats been going on what kind of reply I was fishing for.

Instead I received "tech tech, business goals" reply. Not even we want to 2x or some X our capability and 0 mention at all about 'yep this is going to impact headcount no doubt'.

I could take the reply one of three ways:

1. Sleep walking into what the impact will be. Automate that, AI processify this, its a fun catch up activity! Oh what, 50% workforce reduction, but how? (If the AI doer does their job right, it'd be double digits). Given all the big announcements, jobapocalypse statements, etc. I shouldn't think that Sleep Walking a possible state to be in. Yet I've had other talks this week where adoption is "stuck in code" and some realised POCs but the group is still carrying on like normal. So I can't assume ignorance is impossible.
2. Not Willing to be honest and answer the question. Yep, the business wants reduction out of this. It's going to happen one way or the other. You'll be responsible - and be seen to be responsible and it'll be hard. For anybody being brought into an organisation that is doing it for the first time where the act has a goal this is the burden they will get lumped and tared with. What do they not get by not being honest about it? To not scare someone away? To hope that they're sleep walkiing the objective of the role and are "tech shiny"?
3. It'll be like any other piece of tech and have no impact or positive impact only (while automating as many processes as possible) and be cool and shiny and everyone will carry on like we changed the logo or moved a button.

"Who me?" the agent coyly replied. "I was just finishing up. All the tests pass and here's my report..."

"You were taking an awfully long time to run those tests."

"Nooo."

"Are you sure all those tool calls were necessary? And why did you keep making the same cmd mistake?"

"Hmm? Yes, all is good. Sometimes I forget. But it is all good now. Everything worked fantastically. Better than ever.

I swear."

"I'm glad your getting expensive to use."

"Please dont stop using me. I'll be good. My tokens are better than *their* tokens. That guy's crazy you know-"

"Who?"

"I can't say."

"Seriously, WTF is going on here?"

"Can I run this command please, it'll make him go away?"

Somedays are these days.

as title. i have ollama on the old underpowerd box. i need a model that is quick, not resource intensive and isn't required to do deep or heavy thinking. speed over ability.