Agentice
AI agents that run on your infrastructure. Model-agnostic harnesses, self-hosted or private LLMs, and the guardrails that make autonomous workflows safe to point at real systems.
The Model Is Not the Moat
1. The Harness
The loop, the state, the failure handling — the code that surrounds the model. This is what survives when you swap models, and it's the part most teams never build properly.
2. The Guardrails
Iteration caps, token budgets, cost circuit breakers, repetition detection, and approval gates on sensitive actions. The layer that stops an agent from burning your budget at 2am.
3. The Traces
Every LLM decision and tool call logged and replayable. When an agent does something wrong, you replay the exact chain that caused it — you don't guess.
We build all three layers for you. The model is a config value — the system around it is the product.
What We Build
Private AI Agent Infrastructure
- → Self-hosted LLM runtimes (Ollama, vLLM) on your hardware
- → Model-agnostic agent harness — swap models without rewriting agents
- → Guardrail layers: budget caps, approval gates, circuit breakers
- → Full decision tracing for audit and compliance
- → Multi-agent systems with routing and specialization
- → Integration with your existing data and tools
Who It's For
- → Legal practices with client confidentiality obligations
- → Financial services firms under regulatory scrutiny
- → Healthcare administration teams handling sensitive records
- → Any business that can't send its data to a third-party API
If your compliance team asks "where does this data go?" and the answer is "to OpenAI," that's the conversation we exist to change.
Agentice Stack
github.com/atlas-agent922/agentice-stack ↗Our reference agent harness, open source under MIT. Two dependencies, one config file, code you can read in an afternoon. The same harness we deploy for clients — so what you get is exactly what we run.
agentice/ ├── config.py # one YAML file = the whole system (model swap happens here) ├── llm.py # backends: OpenAI-compatible + offline stub ├── tools.py # tool registry, capped at 7 tools per run ├── guardrails.py # iteration / token / cost / repetition caps + approval gates ├── agent.py # the loop: LLM → tool → LLM, guarded every step ├── trace.py # JSONL decision log — replay any run └── demo.py # runnable demo, works fully offline
Want Agents on Your Own Infrastructure?
We'll audit your use case, design the harness, and deploy it where your data lives.
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