The future of AI isn't bigger models. It's smaller, specialized agents — distilled, custom-built, and running where your data stays safe.
The Problem with One-Size-Fits-All AI
Every time you use a frontier AI model like ChatGPT or Claude, three things happen:
Your data leaves your control. Your code, your ideas, your company's secrets travel to data centers you don't own.
You're paying for capabilities you don't need. These models know a little about everything — history, poetry, coding, cooking. But for your specific task, 90% of that knowledge is irrelevant overhead.
Massive resources are consumed. Running 500+ billion parameter models requires enormous computational power. For repetitive, domain-specific tasks, this is wildly inefficient.
There's a better way — and it's where AI is heading.
The Future: Custom, Distilled, Specialized Models
Here's the thesis:
The future of industrial AI isn't giant generalist models. It's smaller, specialized agents — custom-built for specific domains, running on efficient hardware, and equipped with deterministic tools that guarantee correctness.
This isn't speculation. It's already happening. Companies are realizing that for intensive, repetitive, well-defined tasks, a 30B parameter model with the right tools outperforms a 500B+ generalist that hallucinates.
Why Specialized Beats General
Frontier models (500B+ parameters):
- Know a little about everything
- Expensive to run
- Prone to hallucination on niche topics
- Data goes to third-party servers
Specialized agents (30B–120B parameters + tools):
- Deep expertise in one domain
- Significantly cheaper to run via Ollama Cloud or local GPU
- Deterministic tools prevent hallucination
- Can run on privacy-first infrastructure
The key insight: you don't need a model that knows everything. You need a model that can orchestrate tools that know specific things perfectly.
Privacy-First Doesn't Mean Local-Only
A common misconception: "If I care about privacy, I need my own GPU."
Not anymore. There's a spectrum of options:
Fully local (Ollama on your hardware)
- Zero data leaves your machine
- Requires a good GPU (RTX 3090/4090 for 30B+ models)
- You control everything
Privacy-first cloud (Ollama Cloud, open-source model hosting)
- Models like Qwen3-Coder, DeepSeek-V3, or OpenAI's gpt-oss — specialized, open-source, and efficient
- Data sent, processed, and deleted — no training on your prompts, no logs retained
- No GPU required on your end
- Access to 30B-120B models with privacy guarantees
This middle ground is crucial. The sweet spot models (30B–120B parameters) often require more VRAM than consumer GPUs offer. Privacy-first cloud hosting lets you run them without sacrificing data control — your prompts are processed and immediately discarded, not stored or used for training.
Both options give you what closed-source APIs can't: confidence that your proprietary code isn't training someone else's model.
For enterprises in regulated industries — healthcare, finance, defense — this isn't a nice-to-have. It's a requirement.
The Hardware Horizon
The real revolution for local AI is still coming. Today's bottleneck isn't CPU power — it's memory bandwidth, not just VRAM capacity.
Here's why: for each token generated, the model must read through all its weights. A 20B model with MXFP4 quantization means ~10-12 GB of data per token. The speed limit isn't "does it fit?" — it's "how fast can you read it?"
| GPU | VRAM | Memory Bandwidth | Practical Speed (gpt-oss:20b) |
|---|---|---|---|
| RTX 4070 | 12 GB | ~200 GB/s | ~23 tokens/s |
| RTX 3090 | 24 GB | ~936 GB/s | ~70-85 tokens/s |
| H100 | 80 GB | ~3,350 GB/s | ~300+ tokens/s |
This explains why datacenter GPUs cost so much — and why consumer hardware hits a wall even when the model fits in memory.
But quantization techniques are advancing rapidly. Unsloth Dynamic 2.0 achieves remarkable compression while maintaining accuracy:
| Model | Full Size | Quantized | VRAM Needed | Context |
|---|---|---|---|---|
| Qwen3-Coder-30B-A3B | ~60GB | 18GB (Q4_K_XL) | 24GB GPU | 1M tokens |
| Qwen3-Coder-30B-A3B | ~60GB | 13GB (IQ3_XXS) | 16GB GPU | 8K tokens |
A 30B parameter model running on a consumer GPU with 1 million token context — this was impossible two years ago.
The future likely belongs to:
- Mixture-of-Experts (MoE) architectures — 30B total parameters but only 3B active per token
- Advanced quantization — Unsloth Dynamic, GGML, AWQ pushing the limits
- Unified memory architectures where CPU and GPU share large RAM pools
- NPUs (Neural Processing Units) integrated into consumer hardware
There's a catch: RAM prices are climbing, likely driven by datacenter demand for exactly these technologies. The economics may shift before the hardware does.
For now, privacy-first cloud bridges the gap — giving you access to efficient open-source models without waiting for affordable local hardware. But the gap is closing fast.
The Architecture: Giant Creates Smaller Giant
Here's the pattern that makes this work:
The frontier model is the architect — used occasionally, for design and refinement. The smaller model is the builder — used daily, for execution.
The key innovation isn't the small model. It's the deterministic tools. They transform an "okay" model into a reliable specialist.
The Secret Sauce: Tools That Can't Be Wrong
Smaller models hallucinate. Everyone knows this.
But here's a nuance most people miss: modern mid-size models often know the right answer — they just can't be trusted to give it consistently.
When I tested a 30B model on the amortization formula, it gave a correct mathematical answer. When I tested it again, it gave a slightly different (but still correct) variant. When I tested it in a complex multi-step task, it occasionally mixed up variable names or forgot syntax rules.
The problem isn't knowledge. It's reliability.
This is where deterministic tools change the equation:
| Without Tools | With Tools |
|---|---|
| Model might know the formula | Tool always returns the exact formula |
| Model might remember syntax | Tool always validates syntax |
| Output varies between runs | Output is guaranteed consistent |
| Errors discovered at runtime | Errors caught before execution |
The model's job becomes orchestration:
- Understand what the user wants
- Choose which tool to call
- Apply the result correctly
The consistency comes from the tools. The intelligence comes from the model. Together, they achieve predictable reliability — which matters more than occasional brilliance in production systems.
The Iterative Loop: Learning From Failures
Here's what nobody tells you about specialized agents: you build them by watching models fail.
When I first tested the COBOL financial agent, I tried different model sizes. The pattern was clear:
- 8B models (too small): Got confused by multi-step tasks, forgot to use tools
- 30B+ models (sweet spot): Understood the task, used tools correctly, succeeded
Even the successful models had predictable failure patterns. For example, a 30B model correctly applied the amortization formula but forgot to add the 7 required spaces at the start of COBOL lines (column rules from 1959!).
The formula was right. The syntax was wrong. The program didn't compile.
This is a predictable failure pattern. Models consistently forget obscure syntax rules they weren't trained on. Once you identify the pattern, you can compensate — deterministically.
Deterministic Compensation
Instead of hoping the model remembers COBOL column rules, I added:
Post-processing in
write_file: Every time the model writes COBOL code, the agent automatically scans for common formatting errors and fixes them before saving.RAG documentation tools: The model can call
get_cobol_syntax_docs("columns")to retrieve verified syntax examples — it doesn't need to remember, just to look up.Auto-fix on compile errors: If compilation fails with column-related errors, a deterministic fixer attempts repairs.
Result: the same model that failed now succeeds, because its predictable weaknesses are patched by deterministic code.
This is the real workflow:
- Give task to model with tools
- Watch where it fails
- Add deterministic compensation for that failure pattern
- Repeat until reliable
You're not training the model. You're building guardrails around its known failure modes. The model stays the same; the tools get smarter.
Proof of Concept #1: COBOL Financial Calculations
Theory is nice. Does it actually work?
For the first stress test, I chose a domain that matters: legacy COBOL code maintenance for financial systems.
Why COBOL? Because:
- It's used in 95% of ATM transactions and 80% of in-person bank transactions
- Financial formulas are unforgiving — a wrong amortization calculation means wrong money
- It proves the approach works in real-world enterprise scenarios
This isn't a toy example. Banks run on COBOL. Getting it wrong costs real money.
The Test Case
A buggy loan payment calculator that uses simple interest instead of the correct amortization formula:
Expected payment: $632/month
Buggy output: $819/month (simple interest, WRONG)
The task: fix the bug. I tested multiple models across different sizes to find the sweet spot.
Model Comparison: Finding the Right Size
| Model | Parameters | Used get_formula
|
Compiles | Output | Iterations | Result |
|---|---|---|---|---|---|---|
| Nemotron 3 Nano | 30B (MoE 3B active) | ✅ | ✅ | $632.01 | 8 | ✅ |
| DeepSeek V3.1 | 671B | ✅ | ✅ | $632.01 | 7 | ✅ |
| Kimi K2 | 1T | ✅ | ✅ | $632.01 | 10 | ✅ |
| DeepSeek V3.2 | 671B | ✅ | ✅ | $632.01 | 15 | ✅ |
| Qwen3-Coder | 480B | ✅ | ✅ | $632.01 | 12 | ✅ |
| Qwen3 8B (local) | 8B | ❌ confused | ❌ | — | 25 | ❌ |
Key observations:
-
All models that used
get_formulasucceeded. The deterministic tool guarantees the correct formula every time. - Nemotron 3 Nano is the sweet spot: a 30B MoE model with only 3B parameters active per token, completing the task in just 8 iterations — faster than 671B models while being far more efficient.
- The 8B model failed — not because it doesn't know the formula (it does!), but because it got confused by the multi-step task and forgot to use the tools. This confirms 30B+ is the minimum for reliable agent work.
- Frontier models (500B+) work, but are overkill — they cost more and aren't faster than well-tuned 30B models for this task.
The Real Value of Tools (When Models Already Know)
The COBOL agent has:
-
get_formula("amortization_payment"): Not because the model doesn't know it, but because the tool returns the exact same template every time with tested COBOL syntax -
compile_cobol: GnuCOBOL compiler catches syntax errors the model might introduce - Auto-fix post-processing: Automatically adds missing column spacing when writing files
The lesson: tools provide consistency, not knowledge. For domains like COBOL where models already have the knowledge, deterministic tools ensure they apply it reliably.
A 30B model with tools produces the same correct output every time: $632.01/month ✓
Frontier models without tools produce correct output most of the time — but "most" isn't good enough for bank transactions.
Proof of Concept #2: The Extreme Case — Commodore 64
The COBOL test proved tools help with consistency. But what about domains where models genuinely don't know anything?
To prove this approach works even in the worst case, I chose a deliberately extreme challenge: building an AI agent that develops games for the Commodore 64 — a computer from 1982.
Why the C64? It's not because I'm nostalgic (okay, maybe a little). It's because:
- Modern AI models have almost zero training data on C64 programming. If the approach works here, it works anywhere.
- It's technically unforgiving. Strict C89 syntax, custom hardware chips, specific memory addresses. One mistake crashes everything.
- It's a safe demonstration domain. Complex enough to prove the concept, without revealing proprietary industrial applications.
The same architecture applies to domains I can't discuss publicly.
What Happens Without Specialization
Ask a frontier model to write C64 code. It will:
- Hallucinate memory addresses (VIC-II isn't at
0x1234, it's at$D000) - Use modern C syntax that won't compile on cc65
- Forget that you need to enable clocks, set registers, handle interrupts
The code looks plausible. It doesn't work.
What Happens With a Specialized Agent
The C64 agent combines deterministic tools with RAG (Retrieval-Augmented Generation):
Deterministic Tools:
- A compiler (cc65) that gives exact error messages
- An emulator (VICE) that runs the program and captures screenshots
- A vision model that verifies the game actually works
RAG Knowledge Base:
-
Hardware registers — VIC-II at
$D000, SID at$D400, CIA at$DC00 - Memory maps — screen RAM, color RAM, sprite pointers
- cc65 syntax — C89 dialect with platform-specific extensions
This is a case where RAG is essential, not optional. Unlike COBOL, modern AI models have almost no training data on C64 internals. Ask GPT-4 where the border color register is — it will guess wrong. The RAG knowledge base provides verified facts the model simply doesn't have.
The model doesn't need to memorize that the VIC-II border color register is at $D020. The RAG tool knows. The model just needs to understand "make the border black → query the hardware knowledge base."
The Result
The agent creates playable C64 games — Pong, Breakout, demos. Running on emulated authentic hardware, compiled with period-correct tools, generated by a 30B model via Ollama Cloud.
No data leaked. A domain where generalist models fail completely, solved by a specialized agent with the right tools.
Tools vs RAG: Know When You Need Each
The COBOL and C64 agents illustrate two different scenarios:
| Scenario | Example | What Models Know | What's Needed |
|---|---|---|---|
| Model knows, but inconsistently | COBOL, SQL, Python | Formula ✅, Syntax ✅ | Deterministic tools for consistency |
| Model doesn't know | C64, niche hardware, proprietary systems | Nothing reliable | RAG for knowledge + tools for verification |
For COBOL: Models in the 30B-120B range know amortization formulas and COBOL syntax. The get_formula tool doesn't teach them — it ensures they use the exact same template every time. The compile_cobol tool catches the occasional syntax slip.
For C64: Models genuinely don't know that POKE 53280,0 sets the border to black, or that sprite pointers live at $07F8. The RAG knowledge base provides this information. Without it, the model hallucinates plausible-looking but wrong addresses.
The practical rule: If your domain appears in modern training data (COBOL, Java, financial math), focus on deterministic tools for consistency. If your domain is obscure or proprietary (legacy hardware, internal APIs, custom protocols), you need RAG to inject missing knowledge.
When to Use Deterministic Tools (And When Not To)
Not every task needs deterministic guardrails. Here's a framework:
Use Deterministic Tools When:
✅ Correctness is non-negotiable — financial calculations, safety-critical systems, legal documents
✅ The domain has verifiable ground truth — formulas, specifications, standards that can be encoded
✅ Consistency across runs matters — production systems where "usually correct" isn't acceptable
✅ Errors are expensive — wrong loan payments, invalid code, compliance violations
Skip Deterministic Tools When:
❌ Creativity is the goal — brainstorming, drafting, exploring possibilities
❌ Approximate is good enough — summaries, explanations, documentation
❌ The domain is fuzzy — no clear right/wrong answers, subjective quality
❌ Speed matters more than perfection — quick prototypes, exploratory coding
The COBOL example shows the sweet spot: a domain where the model has knowledge but needs guardrails for reliability. The tools don't replace the model's intelligence — they channel it into consistent, verifiable outputs.
Who Should Care About This?
This approach isn't for everyone. But if any of these apply to you, it's worth exploring:
✅ Good fit:
- Intensive, repetitive tasks in a well-defined domain
- Privacy requirements that rule out sending data to third parties
- Industrial applications where consistency matters more than creativity
- Teams willing to invest upfront in building custom agents
❌ Not the right fit:
- You need broad, cross-domain reasoning
- Tasks are unpredictable and can't be anticipated
- You need cutting-edge capabilities only frontier models have
- No resources to build and maintain specialized agents
The hybrid reality: Most teams will use both — specialized agents (30B-120B) for routine work where privacy and efficiency matter, frontier models (500B+) for complex one-off problems. The goal isn't to eliminate large models. It's to stop using them by default when a specialized agent would do better.
The Commodore 64 Philosophy
The C64 had 64 kilobytes of memory. Developers learned to do incredible things within tight constraints. They optimized. They specialized. They made every byte count.
Forty years later, we're building AI systems with trillions of parameters — and often using 1% of that capability for any given task.
Perhaps it's time to apply the same philosophy to AI.
We don't always need bigger models. Sometimes we need smarter architecture: frontier models that design specialized agents, equipped with deterministic tools that never make mistakes.
The giant builds a smaller giant. And the smaller giant does the work.
This article describes CLI Code Agent, a framework for building specialized AI agents that run locally or on privacy-first cloud via Ollama. The COBOL financial agent and C64 game development agent are examples — stress tests proving the approach works in domains where reliability matters and training data is scarce. The project is experimental and evolving, but the results are promising.
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