Autonomous AI Agents Built for Real-World Work
Move beyond conversational chatbots. Xarwiz AI agents inspect workspaces, execute sandboxed bash commands, call external APIs, query vectorized knowledge, and self-correct on errors with complete deterministic safety.
Architected for Production Reliability
Consumer chat agents break down when given complex file manipulation tasks. Xarwiz builds an isolation harness around models so tasks execute reliably.
Sandboxed Tool Execution
Agents are equipped with native I/O tools: read_file, write_file, list_dir, and bash_command. All calls are contained in isolated tenant directories with non-root privileges.
- Strict path-traversal prevention
- Per-command execution timeouts
- Comprehensive JSON audit trails
Dual-Tier Vector Memory
Combine immediate token-buffered short-term context with long-term semantic embeddings stored in PostgreSQL using pgvector (1536-dimensional cosine similarity).
- Persistent knowledge across sessions
- Tenant-isolated embeddings partition
- Sub-50ms vector query retrieval
Self-Healing Error Correction
When code execution fails or an API returns an HTTP 4xx/5xx code, the agent analyzes the exact error payload, modifies its approach, and retries automatically up to configured limits.
- Stacktrace parsing & error analysis
- Configurable retry backoff policies
- Human-in-the-loop escalation gates
Multi-Agent Orchestration
Coordinate specialized agent roles within unified workflows. Pass structured outputs from a data extraction agent directly into an analytical or code synthesis agent.
- Visual DAG node linking
- Typed JSON schema data contracts
- Parallel branch execution
Core Native Agent Tools
Every tool available in Xarwiz is backed by concrete backend implementations with strict security containment.
| Tool Identifier | Execution Scope | Security Controls | Typical Use Case |
|---|---|---|---|
read_file | Tenant sandbox workspace | Strict path resolution, size limit enforcement | Inspecting source code, configs, CSV data, logs |
write_file | Tenant sandbox workspace | Overwrite confirmations, path whitelist | Generating scripts, config updates, markdown reports |
list_dir | Tenant sandbox workspace | Non-recursive depth limiting, hidden file filtering | Exploring directory trees, verifying generated assets |
bash_command | Sandboxed child process | Timeouts (max 30s), non-root execution, forbidden commands filter | Running unit tests, code linting, CLI scripts |
rag_query | PostgreSQL pgvector + BM25 | Row-level tenant isolation, cosine similarity cutoff | Querying indexed company policies and PDF documents |
Enterprise Execution Guardrails
Deploy autonomous agents with complete peace of mind. Every action is gated by deterministic policies, non-root sandbox execution, and immutable audit logging.
Workspace Chroot Sandboxing
Agent operations are strictly confined to tenant directory roots. System-level paths like /etc, /var/run, or host environment variables are physically inaccessible.
Human-in-the-Loop Approval Gates
Configure automated pausing for sensitive tasks. If an agent attempts to execute high-impact code changes, deploy to production, or dispatch external webhooks, operator approval is required.
Immutable JSON Audit Trail
Every reasoning thought, tool invocation, parameter payload, execution duration, and return output is indexed into tamper-resistant audit logs for full compliance tracking.
Resource Quotas & Circuit Breakers
Prevent runaway execution loops with strict limits on maximum reasoning turns, token consumption ceilings, per-minute tool execution budgets, and command timeout thresholds.
AI Agents Technical FAQs
Clear answers regarding agent execution, sandboxing, and enterprise governance.