The tools we engineer with

A self-improving agent runtime we deploy for long-lived roles: persistent memory, reusable skills, browser automation, and multi-agent delegation. Its learning loop creates skills from experience and improves them during use. That is why it runs where the work is open-ended, not scripted

An open-source agent framework that turns LLMs into autonomous systems: executing code, managing persistent state, and interacting with external APIs. Runs locally, connects to 20+ messaging channels, supports any model provider. We use it when the agent needs to live where the work happens, not behind a dashboard

Stateful orchestration for multi-step reasoning. We choose it when an agent must keep context across tool calls, branch, and recover from failure without losing state. Every node is inspectable and every edge is traceable, which is non-negotiable when a business process depends on the agent

Lightweight multi-agent coordination. We use it when specialized agents need to collaborate on independent subtasks toward one shared goal: role assignment, task delegation, and inter-agent communication without the overhead of a full orchestrator

Precision reasoning for complex analysis, verification, and code synthesis. Chosen when the task demands strict adherence to system boundaries, structured thinking, and deterministic decision trees

High-throughput general-purpose reasoning and multimodal document extraction. We bind frontier models to typed JSON schemas and tool-calling interfaces so they execute within strict operational envelopes

Cost-optimized reasoning models and self-hosted architectures (DeepSeek-R1, Llama 3.3) for high-throughput execution where zero data retention, VPC privacy, or air-gapped infrastructure is required

Low-latency reasoning for real-time agent workflows. Grok's tool-calling efficiency and large context windows suit agents processing long conversation histories before acting

Node-based workflow engine for deterministic, auditable automation. Handles API orchestration, data pipelines, human-in-the-loop approval gates, and event-driven triggers. Every workflow is version-controlled, testable, and deployable through CI/CD. Our default for any automation that needs to be governed

Visual platform for building AI agents and automation across 3,000+ apps. Used when rapid integration breadth outweighs the need for custom logic. Strong visual scenario builder with branching, routing, and error handling. Complements n8n where connector availability matters most

Rapid integration layer for connecting SaaS tools with minimal overhead. Used for high-volume, low-complexity automations where speed of connection matters more than custom logic. We wrap Zapier workflows with monitoring so they stay observable like the rest of the stack

Open-source visual agent builder supporting hundreds of LLMs with built-in RAG, ReAct, and function calling. Used for rapid prototyping of agent workflows before committing to a production orchestration layer. Its visual debugger speeds up prompt iteration and tool configuration

Persistent memory stores for agent context. Enable agents to recall past interactions, retrieve relevant knowledge, and maintain state across sessions. We use vector search for semantic retrieval and structured metadata filtering to narrow results before they reach the model

Every decision, API call, and workflow branch is logged and measurable. Execution latency, context size, token usage, and failure modes are surfaced through structured logs and dashboards. We treat observability as a non-negotiable layer of every deployment, regardless of the underlying model or framework

Every autonomous agent and automated workflow operates under a verifiable identity. RBAC binds each agent to scoped permissions, defining exactly which data it can read, which APIs it can call, and which operations it can trigger. No agent acts outside its authorized envelope