Agentic AI development
Agentic AI development company worldwide.
The demo took a week and wowed the room. Three months later it is still not in production. That is the moment most companies call us.
We design, build and operate teams of AI agents for real workflows: customer operations, research, document processing, sales ops. The difference between our agents and a stalled pilot is everything around the model: evaluation suites, guardrails, monitoring, cost controls, and human escalation paths. Gartner expects more than 40 percent of agentic AI projects to be canceled by 2027. Ours ship, because we build the operating system, not just the agent.
What you get
- Multi-agent workflow design and orchestration
- Eval suites: accuracy as a number, tracked per release
- Guardrails, cost controls and observability
- Human escalation paths and review queues
- Integration with your systems of record
- Runbooks and handover, or fully managed operation
How it runs
Map
Inside the workflow: where decisions happen, what data exists, what failure costs.
Prove
A working agent on real cases with an eval harness. You see the accuracy number before you scale it.
Harden
Guardrails, monitoring, escalation, cost ceilings. The unglamorous half that makes it production-grade.
Operate
Ship and run: dashboards, weekly eval reports, and humans in the loop where the stakes demand it.
From our production work
- An autonomous AI newsroom we operate researches, writes and publishes every day at under $2 per article.
- Document agents read hundred-page reports and surface risk in minutes, with every claim traceable to a page.
- Voice agents run structured interviews end to end, with humans reviewing edge cases.
Which agent frameworks do you work with?
OpenClaw, Hermes Agent, LangGraph and the Model Context Protocol, plus our own harnesses where a framework adds more surface than value. Models: Claude, GPT, Gemini, and open weights like Llama, Qwen and Hermes when data residency or cost demands it.
How do you keep agents from going off the rails?
Structured tool permissions, deterministic checks before irreversible actions, cost and rate ceilings, full audit logs, and human approval gates on high-stakes steps. Agents propose; rules and people verify.
Our agentic pilot stalled. Can you take it over?
Yes, that is our most common starting point. We audit what exists, add the missing eval and guardrail layer, and either productionize it or tell you plainly why it will not work.
What does the path to production look like?
The entry audit tells you exactly what your workflow needs; a working agent on real cases comes fast, and hardening depth depends on your integrations and review requirements.
Bring us the workflow. Leave with a plan.
One call. We will tell you honestly what AI can and cannot do about it, and what it costs to find out.