Pyrodit Stack By Mindtro
Enterprise Agentic Runtime & AI Security
Pyrodit Agentic Runtime provides durable execution for AI agents. Scan Lab evaluates security and practical reasoning; Guard applies runtime policies. Runner, Studio and Essentials extend the stack with local execution, visual workflow design and a private AI workspace.
Start with the need
A team, a process or an AI application. Choose where to start; together we will identify the products and integrations your work needs.
Operations & engineering
Coordinate agents and tools to gather data, compare options and prepare a result. Define where a human decision belongs in the process.
A result you can review alongside its reasoning and execution history.
Security & AI product teams
Use Scan Lab to assess attack responses and practical reasoning; define request and tool-action policies with Guard.
Reviewable security findings and runtime policy decisions.
Teams & knowledge owners
Bring scattered documents and recurring questions into an assistant experience grounded in approved sources.
Answers with sources to inspect and drafts ready for team review.
Build the combination around your process. Choose the products, connect the systems and set data access and approval boundaries for your organisation.
Pyrodit Stack
Each product has a clear job. Start with the capability you need and expand the scope as your work grows.
Run business workflows
Durable execution for AI agents: preserve progress through interruptions, wait for human decisions and coordinate long-running work across your systems.
Durable workflows with single-process or distributed execution, scheduled waits, human approvals and connected tools and agents.
Evaluate AI security & reasoning
Evaluate how AI systems respond to attacks, solve tasks and handle feedback—with traceable evidence.
Start with controlled tests on authorised targets. Interpret findings alongside the coverage actually completed.
Govern actions with policy
Define which AI requests and tool actions are allowed, which need review and how sensitive data should be handled.
Evaluate core policy capabilities. Confirm the development status of gateway, SDK and adapter connections before deployment.
Execute local tools
Connect agents to files, commands, browsers and selected applications through explicit permissions. Follow each operation from request to recorded outcome.
Local files, commands, browser sessions and selected applications, with explicit permissions and recorded operation outcomes.
Design visual flows
Draw the workflow before you run it. Connect nodes, shape conditions and data mappings, and share a flow your whole team can inspect.
The visual editor includes a local simulation preview. Runtime publishing and execution integration are evaluated separately for your deployment.
Connect teams with knowledge
Give growing teams a ready path to private AI, grounded knowledge and governed assistants.
Start with controlled local evaluation. Wider rollout requires validation of sign-in, multi-user deployment and data connections.
Agentic Runtime · Procurement example
Consider a purchase request: 100 units, delivered within 7 days. Finance evaluates cost; operations evaluates timing. Runtime brings their work into one flow.
Set budget and delivery requirements for 100 units.
Economy: USD 10,050, 12 days. Express: USD 10,200, 4 days.
For an extra USD 150, Express meets the 7-day delivery limit.
The procurement owner reviews the recommendation and its basis.
Cost, timing and reasoning in one result.
Based on a supplied Runtime execution result. No external order was placed in this example; human approval is the next business step.For engineering teams
Connect Pyrodit to your own application with the Python Client SDK. Build tools for your business systems with the Plugin SDK and add organisation-specific runtime checks with the Enterprise SDK.
Start with us
Begin with an evaluation scope that fits your work and gives you a result you can measure.
Identify the people, task, data and decision boundaries involved.
Choose products, connections, deployment and the steps that need human approval.
Use output quality, exceptions and review time to decide on the next step.
