Deploy Llama as a Governed AI Agent
Llama (Meta) is the open-source leader — 405B to 8B variants, self-hostable, fine-tunable, huge developer adoption. But open-source doesn't mean production-ready. To run Llama reliably you need a harness that handles hosting, credentials, approvals, retries, memory, and audit trails. SchemaBounce provides that layer.
Why Llama Needs a Harness in Production
Llama 3.3 405B and Llama 3.1/3.2 models are everywhere because you can download them, fine-tune them, and own the weights. That's the superpower and the trap.
Self-hosting proves the trap:
- You own the ops. GPU provisioning, scaling, model updates, and latency are yours now.
- Open weights ≠ governed actions. Llama can call tools, draft emails, and update your CRM — but who approves that write? Who logs it? Who retries when it fails?
- Fine-tunes drift. Your tuned Llama behaves differently than base. You need receipts and observability to catch regressions.
- Multiple Llama sizes = routing decisions. 8B for classification, 405B for reasoning — you need a harness that routes and tracks cost per agent, per run.
The model is free. Production isn't.
Llama via SchemaBounce: How It Works
SchemaBounce is model-agnostic. Llama runs as a hosted agent behind the same governed runtime as Kimi, Claude, or GPT-4. Bring your own weights or use a hosted endpoint — the harness stays the same.
# Example: Llama agent on SchemaBounce
from schemabounce import Agent
agent = Agent(
model="meta/llama-3.3-405b",
name="llama-ops-agent",
harness={
"memory": "adl", # persistent, workspace-scoped
"approvals": "inbox", # human review before external actions
"credentials": "vault", # secret:llama_api_key, never in code
"observability": True # tokens, cost, receipts per run
}
)
# Self-hosted variant: point to your endpoint
# agent = Agent(model="meta/llama-3.1-8b", endpoint="https://llama.internal/v1")
result = agent.run("Classify inbound tickets and draft replies — require approval before sending")
What the harness adds:
- Hosting flexibility: Use Meta's API, your own GPUs, or a provider (Together, Fireworks, Groq) — harness works the same.
- Credentials by reference:
secret:llama_api_keyinjected at call time, never in code or logs. - Action approvals: External actions pause for Inbox approval. Only the Inbox counts — chat replies don't approve.
- Receipts: Every tool call recorded with inputs, outputs, latency, and approval status.
- ADL memory: Findings persist across runs, so Llama agents share context without re-prompting.
- Inter-agent mesh: Llama agents coordinate with Kimi, Claude, or GPT agents via native messaging.
Llama vs Running Raw or Self-Hosted
| Capability | Raw / Self-Hosted Llama | Llama on SchemaBounce |
|---|---|---|
| Model weights | ✅ You host | ✅ You host or use hosted API — same harness |
| Fine-tuning | ✅ Full control | ✅ Full control (harness is model-agnostic) |
| Credential management | You (env vars / secrets) | Vault, by-name reference |
| Human approval before send | You build | Built-in Inbox gate |
| Audit log / receipts | You build | Built-in, per-action |
| Persistent memory | You build | ADL (workspace-scoped) |
| Retry & error handling | You build | Built-in with backoff |
| Cost & token observability | Manual | Per-agent, per-run, per-model |
| Multi-model routing | You build | Native (route by task to 8B/70B/405B) |
| Inter-agent handoff | Not possible | Native messaging |
You keep open-source control. You stop rebuilding the operating layer.
When to Use Llama in a Harness
Llama shines where openness and cost control matter:
- On-prem or VPC deployment: Keep data in your network, still get governed approvals and receipts centrally.
- Fine-tuned specialists: A tuned 8B classifier that triages tickets + a 405B reasoner that drafts responses — routed by the harness, observed per run.
- Cost-sensitive scale: Run 8B for high-volume simple tasks, 405B only where reasoning matters — with cost tracking to prove the tradeoff.
- Community + control: Largest open ecosystem (Hugging Face, vLLM, Ollama) with a production harness so you don't operate the glue code.
If your strategy is open-source, Llama plus a harness is how it survives production handoff.
FAQ
Is Llama supported on SchemaBounce?
Yes. Use Meta's API, Together, Fireworks, Groq, or your self-hosted endpoint. Set the model to meta/llama-3.3-405b (or 3.1-70B/8B) and store credentials as vault secrets.
Can I bring my own fine-tuned Llama? Yes. Point the agent to your endpoint URL. The harness is model-agnostic — approvals, receipts, memory, and observability work identically.
How does hosting work? SchemaBounce doesn't lock you to one provider. Bring your own GPUs or use any OpenAI-compatible Llama endpoint. You pay the provider for inference and SchemaBounce for the operational layer.
Can Llama coordinate with other models? Yes. Llama agents message Kimi, Claude, or GPT agents natively and share structured records through ADL.
What about 8B vs 70B vs 405B? Use all three. Configure separate agents per size and route by task complexity. The harness tracks cost and success rate per model so you can tune routing with data.
This page was written with AI assistance and reviewed by the SchemaBounce team. Ready to run Llama in production? Request a Production Readiness Report.