Agent systems
Tool-using systems that research, decide and execute multi-step work with explicit permissions and human approval at critical actions.
Infravane is an applied AI lab and systems company. We design, deploy and operate autonomous agents, intelligence products and adaptive growth infrastructure.
Scoped autonomy.Observable decisions.Measurable outcomes.
Systems
Three system classes. Each is specified with problem, control model and outputs - not feature slogans.
Tool-using systems that research, decide and execute multi-step work with explicit permissions and human approval at critical actions.
Interfaces and internal products that turn fragmented information into traceable decisions, workflows and measurable outcomes.
Infrastructure that connects customer signals, experimentation and execution into a continuously improving operating loop.
Selected systems
Honest labels only. No invented customers, metrics or case-study theatre. These are real systems designed and operated by the team behind Infravane.
Autonomous agent stack with KeepAlive workers, deadman checks, approval files for high-risk actions, and a filesystem kill switch that blocks buys while always allowing exits.
System detailPublish-only ledger of closed operations for external scrutiny. Separates runtime state from the public record so observers see outcomes, not internal noise.
System detailDistribution and monetization infrastructure for AI products in high-friction verticals: organic channels, payment rails the majors refuse, and source-to-revenue tracking.
System detailDeployment standard
A non-negotiable operating standard for every agent, product surface and growth loop we ship.
Agents act only within a declared domain, tool set and spend envelope. Expansion requires an explicit change, not a silent prompt tweak.
Every consequential action leaves a structured record: inputs considered, policy path taken, tool calls made, and result.
Irreversible, high-cost or externally visible actions require a human signal - file, session, or dual-control - before execution.
Tools are granted narrowly. Credentials are not ambient. Read paths and write paths are separated by default.
Runtime health, failure modes and outcome quality are measured on a schedule, not only after incidents.
Success is defined as a change in a real operating metric - not model vanity scores alone.
Lab
Not a blog. Three concrete research tracks with operational applications. Full lab →
How tool-using agents stay recoverable under partial failure, network loss and human interruption.
Current questions
Product patterns that make model output into a decision object with ownership, review and audit.
Current questions
Closing the loop between live signals, experiments and execution without uncontrolled automation.
Current questions
Company
| Brand | Infravane |
|---|---|
| Legal operator | FERAL SIGNAL LIMITED |
| Jurisdiction | Ireland |
| CRO | 814597 |
| Focus | Applied AI systems and technology products |
| Contact | [email protected] |