Reliable agent execution
Typed state, model routing, tool permissions, recovery, verification, and human approvals for agents that must do more than generate text.
Research output · Agent Runtime + Agent Studio
Applied research & development
Research that becomesworking product.
Algen is an AI product and applied R&D company. We study how agents are built, evaluated, governed, discovered, and trusted—then turn useful findings into open-source infrastructure, commercial products, and production systems.
Research focus
Practical questions around reliable AI agents.
Evaluation and governance
OpenTelemetry-native traces, datasets, scorers, policy checks, incidents, and evidence for understanding and controlling agent behavior.
Research output · Traccia
Agent discovery and assurance
Versioned manifests, maturity, risk, permissions, safeguards, and evidence that help teams assess an agent before adoption.
Research output · Agent Hub + ASCEND
Human-agent decision systems
How agents, existing software, operational data, and people can share decisions with clear authority, explanations, and measurable outcomes.
Research output · DIRECT method + production systems
How we publish
Evidence before claims.
We separate documented facts from hypotheses, describe methods and limitations, and distinguish our own experiments from independent benchmarks. Research is useful when another engineer can inspect the reasoning and apply it responsibly.
Open-source infrastructure
Inspect and contribute to the foundations behind Algen agent systems.
Explore Agent RuntimeProducts
See how research questions become software for building and operating agents.
View Algen productsResearch notes
Read technical analysis, experiments, benchmarks, and implementation lessons.
Read the research notesProduction validation
See how ideas are tested inside real workflows and operational constraints.
View case studiesRecent research notes
What we are examining.
AI Engineering · 2026-09-21
Jev Explained: How TypeSafe’s Decision Model Fits Into AI Agents
A practical look at typed decisions, calibrated probabilities, agent architecture, and the evidence behind Jev’s performance claims.
Engineering · 2026-08-05
Reasoning vs Standard LLMs: When Is It Worth Paying the Thinking Tax?
A local side-by-side benchmark exploring when reasoning models pay off versus standard LLMs — and how to allocate thinking time to task risk.
Engineering · 2026-07-30
Code Is Cheap, Context Is Expensive: The Strategic Power of the Forward Deployed Engineer
95% of enterprise AI pilots fail not because of model quality, but because nobody is sitting close enough to the problem to see what the model actually needs.