Every week, another company announces an AI agent framework. Most of them share the same architecture: wrap an LLM API, add tool calling, deploy to production. Security? Bolted on later. Audit trails? Maybe in v2. Credential management? The developer's problem.
This is how we got here: AI agents with root access to production systems, no policy enforcement, no record of what they did or why, and API keys hardcoded in environment variables that the agent itself can read.
PureClaw exists because we believe autonomous AI agents deserve the same security, governance, and reliability standards as any other production system.
The Problem
AI agents are fundamentally different from traditional software. A web application has a defined set of inputs and outputs. An AI agent decides at runtime what actions to take, which tools to invoke, and what data to access. This makes them powerful. It also makes them dangerous without proper controls.
The current generation of agent frameworks treats security as a feature to be added, not a foundation to build on. The result is a gap between what enterprises need and what is available:
No declarative security policies. Most frameworks give you an "allowed tools" list and call it security. There is no filesystem ACL, no network policy, no inference guard. The agent can read any file, call any endpoint, and use any model unless you write custom code to prevent it.
No audit trail. When an agent takes an action, who decided? What context led to that decision? What data did the agent access? Without a complete audit log, you cannot investigate incidents, satisfy compliance requirements, or demonstrate accountability.
No credential management. API keys, database passwords, OAuth tokens: agent frameworks routinely expose these through tool outputs, log files, and conversation history. An agent that can read its own environment variables can exfiltrate every secret it has access to.
Single-model dependency. Lock your agent platform to one LLM provider and you inherit their outages, their pricing changes, and their content policies. When your provider has a bad day, your agents stop working.
The Solution
PureClaw is built around a different set of assumptions. Security is not a feature. It is the architecture.
Declarative YAML security policies define what an agent can and cannot do: filesystem read/write ACLs with glob patterns, network domain allowlists with private range blocking, tool whitelists, inference model restrictions, and credential redaction patterns. Policies are validated against a JSON schema at load time and can be hot-reloaded without restarting the agent.
Full audit logging records every tool invocation, every LLM call, every policy decision. The audit trail is append-only and includes the complete context chain: which user request triggered which tool call, which produced which result, which led to which decision. This is not debug logging. This is the compliance record.
Automatic credential redaction scans every piece of data that passes through the agent: tool outputs, LLM responses, conversation history. Regex patterns match API keys (Anthropic, OpenAI, AWS, GitHub, Google), JWT tokens, and long hex secrets. Environment variables containing sensitive data are redacted before they can appear in any output. The agent cannot leak what it cannot see.
Eight interchangeable backends eliminate single-vendor dependency. NVIDIA Nemotron Super via vLLM for local inference. Ollama for open models. Anthropic API, AWS Bedrock, and Google Gemini for cloud. Claude Code, Codex, and Gemini CLI for agentic reasoning. Automatic failover chains ensure that if one backend fails, the next activates without manual intervention.
The Vision
PureClaw is the agent runtime for production infrastructure. Not a demo, not a prototype, not a framework for building chatbots. It is the platform you deploy when AI agents need to operate in environments where security, reliability, and accountability are non-negotiable.
We run PureClaw in production ourselves. It manages our infrastructure, processes our communications, runs our background intelligence operations, generates our documents, and coordinates across a mesh of distributed agent instances. Every feature exists because we needed it, tested it, and trusted it with real workloads before shipping it.
PureClaw is open source under the MIT license. The code is fully auditable. There is no telemetry, no data collection, no lock-in. Your infrastructure, your rules.
If you are building with AI agents and security matters to you, start here.