Autonomous Agents
AI Agent Deployment.
We deploy, sandbox, and supervise autonomous agents that decide their own next step — with the boundary of what they may do without asking drawn before anything goes live.
Book Agent Readiness AuditWhat An Agent Is
Not a longer workflow. A different shape.
An automation runs the steps you gave it and stops. An agent reads the state, picks its own next move, and keeps going until the job is actually finished. That single difference is why it needs supervision rather than monitoring.
- 01
Observe
The agent reads the current state — inbox, CRM, files, the result of its own last action — instead of waiting for a trigger you wired by hand.
- 02
Decide
It chooses the next tool call from what it just saw. This is the step a workflow does not have, and the step that makes a boundary necessary.
- 03
Act
It calls the tool for real: sends the mail, writes the record, runs the query. Every call is logged with the reasoning that produced it.
- 04
Check
It reads its own result and either closes the task or loops back to observe. The run ends when the work is done, not when the steps run out.
The Runtimes
Two we deploy, both yours to host.
We are not tied to one runtime. These are the two we run in production, both open source and both self-hostable, so the agent operating your business stays on infrastructure you control.
- OpenClawLocal-first agent runtime. Runs on your own hardware, with a gateway process handling sessions, tool dispatch, and channel routing.github.com/openclaw/openclaw
- Hermes AgentPersistent-memory agent. Carries context across sessions and writes its own reusable skill documents as it solves new problems.github.com/NousResearch/hermes-agent
Proof of Work
Systems We've Shipped
A production agent running a real business — not a demo.
Where This Connects
An agent is rarely the only system you need.
Workflow Orchestration
AI Workflow Automation Services
Replace brittle no-code automations with outcome-owned systems for lead routing, CRM sync, enrichment, and sales operations.
Explore Workflow AutomationAI Search Systems
RAG Chatbot Development
Build retrieval-grounded assistants and AI search systems that return reliable answers and plug into real product or internal workflows.
Explore RAG Chatbot DevelopmentRevenue Operations
CRM Automation Services
Clean up CRM workflows with automated assignment, enrichment, lifecycle updates, and reporting across your operating stack.
Explore CRM AutomationFrom the Lab
Related Reading
Vertical AI Agents and the Shift Beyond SaaS
Why workflow-owning AI products are emerging, where the opportunity is real, and why operations matter more than generic AI wrappers.
Read
When a Workflow Should Become an AI Agent
A practical guide to deciding when deterministic automation is enough and when the problem has become agentic enough to justify a different architecture.
Read
What MCP Is and Where It Fits in AI Integrations
A practical explanation of the Model Context Protocol, what problem it solves, and where it fits in real AI systems.
Read
Agent FAQ
AI Agent Deployment FAQ
Questions teams ask before letting software decide its own next step inside their operation.
What is the difference between an AI agent and an automation?
An automation runs the steps you gave it and stops. An agent reads the current state, chooses its own next step, and keeps going until the job is finished. That difference is why an agent needs a supervised boundary rather than a monitoring dashboard.
How do you stop an agent doing something it should not?
We draw the approval boundary before anything goes live. Every capability the agent has is either something it may do alone or something it must hand to a person, and the actions that cross that line stop and wait for approval instead of running.
Which agent runtimes do you deploy on?
We deploy on OpenClaw and Hermes Agent. Both are open-source runtimes you can host yourself, so the agent, its memory, and its tool access stay inside infrastructure you control rather than a vendor's platform.
Do we need our workflows automated before we add an agent?
Usually yes. An agent is most useful where the underlying systems, data access, and integrations already work. If those are still manual, we normally start with workflow automation and add autonomy once the ground is stable.
How do you test an agent before it runs unattended?
It runs sandboxed first, with its actions logged and its boundary set tight, so you can see every decision it would have made before it is allowed to make them. The boundary widens only for the actions that have proven safe.
Agents are only worth deploying when someone owns what they do.
Book a readiness audit and we will map which parts of your operation an agent should own, where the approval boundary belongs, and what has to be true before anything runs unattended.
30 minutes · founder-led · no deck