Agents that plan, call scoped tools and finish multi-step work in your systems, with human approval where it matters.
\0.1
Agents for real operations
Agentic AI, Engineered to Run in Production
Agents that plan, use tools and act inside your systems, with scoped access, human sign-off and monitoring.
Scroll to explore
Agents are easy to demo and hard to run. They need scoped tools, a queue, evals, traces and a human checkpoint. We design and run that whole system, not only the prompt.
What we do
Your APIs, databases and documents exposed to agents as scoped, permissioned tools, including over MCP.
\0.2
Tools and MCP
Specialist agents with explicit hand-offs and retries, coordinated by event-driven workflows.
\0.3
Agent Orchestration
Approval steps, permissions and test sets, so people stay in control of what agents do.
\0.4
Guardrails and Review
Logs, traces and alerts on every run, so you can see what each agent did.
\0.5
Agent Observability
Python and Rust services, Kafka, Kubernetes and Terraform, plus private hosting of open models where data must stay in.
\0.6
Production Engineering
We take traditional organizations agent-first: train your people, set up a company knowledge base, and hand communication and development work to agents.
\0.7
Agent-First Company
Kainguru
Our platform to host, fine-tune and call AI models through one API, with GPU workers that scale to zero.
Story Sprinkle
Personalized children's stories with characters, narration and images. Live on the App Store.
How we build
Principles we apply to every agent system.
Agents with clear boundaries
Each agent gets only the tools and data its job needs.
Humans at the checkpoints
People approve the actions that carry risk.
Evaluated before trusted
Test sets and reviews measure behavior before an agent gets more autonomy.
Kafka event backbone
Agent jobs as events: decoupled, replayable, retried when a step fails.
Observable end to end
Every run is logged, traced and open to alerting.
Agent-first engineering
Agents write code, open pull requests and run deploys. An engineer signs off.
Infrastructure as code
Terraform, Helm and ArgoCD make every environment repeatable.
Why Dnysus
You work directly with the architect who designs and builds your system. We start from one process, agree how success is measured, and keep scope small.
Direct access to the architect- Success measures agreed up front
- Human approval built in
- Agent-first: code, Git and deploys
- Documentation and handover
- Works on AWS, Azure or DigitalOcean
Who we work with
Same engineering discipline, whatever the starting point.
For traditional organizations
Go agent-first: train your people, set up a shared knowledge base and put agents into daily work.
Plan the transformation
For growing companies
Get a senior architect and a working agent without hiring a platform team.
Start a conversationFounder
Elkhan Shahverdi
Solution Architect and AI Product Builder
Elkhan designs and builds the systems behind your agents, from Python and Rust services and Kafka event streams to Kubernetes and Terraform. He also builds and runs AI products of his own, so the advice comes from operating them.
GitHub
Email
Schedule a CallQuestions
(1)What can an agent actually do for us?
Handle repeatable work such as triaging requests, drafting replies, checking records and updating systems, within permissions you define. Anything risky goes to a person for approval.
(2)Can agents use our documents and data?
Yes. Agents can read your documents, tickets and databases through scoped connections, using vector search where it fits. Access is limited to what each task needs.
(3)How do we keep agents safe?
Narrow permissions, approval steps for risky actions, test sets run before and after changes, and a log of every run. You can see what happened and step in at any point.
(4)How do we measure the result?
We agree a baseline and success measures before building, such as time per task or share handled without rework, then report against them once the agent is live.
(5)Can models run on our infrastructure?
Yes. We host open-source models privately on Kubernetes GPU nodes and can fine-tune them. Licences are checked per model, and we will say so when a hosted model is the better choice.
(6)Can you help our whole company go agent-first?
Yes. We start with one team: train people to work with agents, set up a shared company knowledge base, put agents into everyday communication and delegate development tasks to coding agents under review. Then we roll out what worked, team by team, with clear rules on access and approval.