ORIGIN AI®
We design and deploy autonomous AI agent systems — single agents, multi-agent teams, and full agentic pipelines — that reason through complex tasks, use tools, collaborate, and deliver results without human hand-holding at every step.
Traditional AI responds to a single prompt. Agentic AI is given a goal and figures out how to achieve it — breaking the goal into subtasks, selecting the right tools (search, code execution, APIs, databases), executing them, evaluating the result, and course-correcting until the task is done. The result is AI that operates like a capable employee, not a calculator.
Single agents handle well-scoped tasks autonomously — customer triage, data extraction, report generation. Multi-agent systems deploy specialised agents (researcher, writer, critic, executor) that collaborate via a supervisor or graph-based orchestration, tackling tasks of any complexity without human bottlenecks.
Production agents need memory. We implement short-term working memory (conversation context), long-term semantic memory (vector stores), episodic memory (past task logs), and structured state management — so your agents remember context across sessions, learn from past executions, and improve over time.
Every agent system is different. We select and implement the right architectural pattern based on task complexity, required autonomy, latency constraints, and the degree of human oversight your use case demands.
The foundational pattern: Reason → Act → Observe, looping until the task is complete. The agent reasons in natural language about what tool to call next, executes it, observes the result, and reasons again. Best for focused, tool-using tasks with clear completion criteria.
A supervisor agent breaks down a complex goal and delegates subtasks to specialised sub-agents — researcher, writer, analyst, critic — each with their own tools and system prompts. Results are aggregated, critiqued, and refined. Enables division of cognitive labour at scale.
Built with LangGraph — agents modelled as nodes in a directed state graph with conditional edges. Supports cycles (retry loops), branching (tool selection), and precise human-in-the-loop checkpoints. The most controllable and observable agentic architecture for production systems.
Agents that autonomously navigate websites, fill forms, extract structured data, monitor pages for changes, and interact with web applications — using Playwright, Selenium, or headless browser frameworks controlled by the LLM. Turns any website into an API.
Agents with sandboxed Python / JavaScript execution environments. They write code, run it, observe the output, fix errors, and iterate — autonomously solving data analysis tasks, building scripts, and debugging pipelines end-to-end without a human developer in the loop.
Agents that explicitly reflect on their own outputs, generate verbal feedback on what went wrong, and use that feedback to improve on the next attempt — without retraining. Achieves dramatically higher task success rates on complex, open-ended problems through structured self-critique.
A multi-agent pipeline that researches prospects, enriches CRM data, writes personalised outreach sequences, and schedules follow-ups — running 24/7 without a sales rep touching it. Integrated directly with HubSpot, LinkedIn, and Apollo.
An agent that connects to your data warehouse, writes and executes SQL queries, generates visualisations, interprets findings, and produces a fully-written analyst-quality report — in minutes, not days. Deployed weekly, triggered by data pipeline completion.
An agent that processes incoming patient intake forms, cross-references clinical guidelines via RAG, generates structured triage notes, flags urgent cases, and pre-populates EHR fields — reviewed by a clinician in under 60 seconds per case.
Tell us the task, the tools it needs, and how much human oversight you want. We'll design the right agent architecture, build it, evaluate it, and deploy it — reliably and safely.