Mission

We're IBM Research's proving ground for emerging AI.

We turn emerging ideas into things people can actually try, then use what we learn to help decide what should be built, changed, or stopped.

Writing from the team

Up next · October 28, 2026

A human-operated software factory, not a dark one

Our take on what a software factory is, what it means for the business of software engineering, and how someone builds one on DAM. Two halves: the vision, then the opportunity. The vision the team is arguing for is a **human-operated factory, not a dark one**. The claim is not that software development gets automated away — it is that the collaboration between the person and the agent gets automated, as far as it will go, and that the models are not good enough for anything else today. That makes the interesting question *where* the person belongs rather than whether they do. The answer the team keeps arriving at is a small set of defined touch points — context creation, business and architecture alignment, review and approval — with everything between them automated. Two kinds of alignment do different jobs and should not be collapsed: a technical grill with the agent surfaces unknown problems, a discussion with people creates agreement on direction. The structural argument is that a factory is a **codified process, not a group of agents deciding together**. Implement, review, approve, merge — fixed, which is what makes parallel work safe; freely coordinating agents produce race conditions instead. GitHub is both the channel and the state store, so the whole factory state lives in issues, labels, PRs and commits: stop it, start it again, and it continues without duplicating work, and people join in the same place the agents work. Agents hand off through the channel rather than to each other directly, because direct handover has no confirmation, no shared state and bad timing. Events push work to agents; agents do not wake on a timer and go looking for it, which mostly buys wake-ups that find nothing. The second half is where DAM comes in — which building blocks exist, which are missing, and what has to be added before someone can build their own factory on it. The framing to keep: building blocks are not the story. Start from the audience, then the pain, then the features, then the blocks. And the team is its own first customer — building our own factory on DAM is how we find out what is missing. The post has to take positions against the state of the art rather than describe it: factory.ai, the KubeStellar hive, and how Anthropic build and run their own factories — including that they started with free agent groups and moved away from them. Where those disagree with us, say so and say why.

Radek Jezek, Samantha Dempsey See what's coming →
Agent infrastructure

Agents as Teammates

How our team shares workspaces and context with the agents we work with.

Samantha Dempsey, Matous Havlena, Sarah Miller, Jenna Winkler September 2026 · 4 min read
Introductions

Hello from AI Industrialization

Why we exist, how we work, what we're building, the questions we're still exploring — and what you can expect to read here.

AI Platform Incubation Team September 2026 · 5 min read
Insight

Sovereign AI: why you need a platform

Sovereign AI, and why getting it means running your own platform. Scope to be defined by the author.

Matous Havlena Coming soon
Insight

Operational discipline doesn't run on willpower

Most team inefficiency is not people working slowly. It is people working on the wrong thing, working from missing or wrong context, or working inside a process that quietly wastes their time. All three are visible in the artefacts a team already produces — issues, epics, plans, the trail of how work actually moved — and all three are exactly the kind of pattern an agent will find because it will read every one of them and a person will not. The worked example is the team's own work on issues, epics and planning: what tightening those up actually changed, and what the agent noticed that nobody had written down. The wider argument is that operational discipline normally decays because it depends on someone having the attention to enforce it on a busy week. An agent doesn't have busy weeks. It can keep checking that an epic still has a stated outcome, that an issue carries the context needed to start it, that the plan and the board have not silently diverged — and keep steering the team back rather than filing a report nobody reads. Worth taking positions on: which signals are real and which are process theatre; that the point is to surface the problem to the team, not to score people; and where this stops — an agent can say the context is missing, but it cannot decide what the team should care about this quarter.

Petr Bulanek Coming soon

What we do

Agent infrastructure

DAM, our open source platform for running long-lived AI agents — each in an isolated sandbox behind a zero-trust credential gateway, driven from the browser, the CLI, Slack, or on schedules. We're still exploring where the line falls between what the agent handles and what the platform should.

Inference at scale

The serving substrate agentic research runs on, tuned for the throughput and cost of long-running autonomous work — so experiments are bounded by ideas, not by capacity.

Research validation

Interviews, studies, flow boards, coded prototypes — whatever makes an idea concrete enough to put in front of researchers and challenge before it's real. What breaks tells us what to build, change, or stop.