About

We build AI agents that actually do the work.

Most AI tools organize your tasks. Ours finish them. Agency deploys autonomous agents that triage your inbox, update your CRM, build your reports, and take action across the tools your team already uses — so operators can stop managing software and start leading.

Origin

Built by operators, for operators.

Agency started with a frustration anyone running a growing company knows: the work isn’t the bottleneck. The admin around the work is.

We watched the best operators we knew spend their best hours inside dashboards, inboxes, and spreadsheets. The tools promised productivity. They delivered paperwork. Every new tool added a tab, a notification, a thing to remember to check.

So we built the thing we wanted: agents that sit alongside your team, with the same context, the same tools, and the patience to do the work the same way every time. No dashboards to configure. No low-code flows to maintain. Just outcomes.

Today we serve operators at fast-growing teams who measure their agents the same way they measure their people: by what actually got done.

What we stand for

Four tenets we won’t compromise on.

Work, not workflow.

Our agents take actions across the tools your team already uses. No canvas, no automation builder, no configuration sprawl. They do the thing.

Credible by default.

Every action leaves a trace you can read. Every decision is reviewable. Every integration is auditable. Operators earn trust; so should software.

Harness is the product.

Agents are only as useful as the scaffolding around them — the FSM, the tools, the memory, the recovery loop. We invest heavily in the harness so every agent running on Agency inherits the same reliability floor.

Taste is a feature.

Operators pick Agency because it feels correct — fast, quiet, precise. We treat interface design and output quality as non-negotiable.

Harness

Tool-execution-focused agents.

Agency runs on Agentia — our agent harness built for reliability. Every run leaves structured traces. Every failure becomes a signal. Every session makes the next one better.

FSM Architecture

A finite state machine models every stage of an agent’s life. Thinking, tool execution, error recovery, suspended. Predictable and debuggable.

Python as Tools

Any Python function is a tool. Schemas generate from type hints, dataclasses, and Pydantic models. No boilerplate, no wrapper DSL.

Live Task Plans

Every agent maintains an explicit plan and works top-to-bottom. Visible to humans. Editable mid-run. The opposite of a black box.

Object Memory

Agents store real Python objects — dataframes, session contexts, large results — and pass them between tools by reference. Memory survives tool calls without ever hitting the LLM context.

Recovery with Nudges

When an agent stalls or spirals, the harness injects corrective nudges — first soft, then firm — to steer it back on track. When a tool repeatedly fails, a targeted note redirects the LLM away from futile retries.

Tool Execution

Every tool call is validated, sandboxed, and traced. Pre-validation catches bad arguments before they hit your systems; post-execution capture stores results for the next step. Agents act safely.

Team

Built by operators, for operators.

A small team of engineers and operators. We’ve built infrastructure for some of the hardest customers in the world — now we’re using that discipline to build AI that actually ships work.

Mehmet Ozan Kabak

Co-founder & CEO

Sami Can Tandogdu

Co-founder & COO

Berkay Sahin

Founding Engineer

Backed by

Investors who build.

We’re backed by funds and operators who’ve built and scaled the kinds of companies we sell into.

Angel investors

Marcin Zukowski

Founder of Snowflake

Richard Olver

ex-CrowdStrike VP, ex-Ondat CEO

Wes McKinney

Creator of pandas and Arrow

Steve Ciesinski

ex-President of Stanford SRI

$ agent.run() ── ⚡ skill.exec() ── │ ├── tool.chain() │ └── return data ── > _ ready ── ░▒▓ ██ ▓▒░ ── $ agent.run() ── ⚡ skill.exec() ── │ ├── tool.chain() │ └── return data ── > _ ready ── ░▒▓ ██ ▓▒░ ── $ agent.run() ── ⚡ skill.exec() ── │ ├── tool.chain() │ └── return data ── > _ ready ── ░▒▓ ██ ▓▒░ ── $ agent.run() ── ⚡ skill.exec() ── │ ├── tool.chain() │ └── return data ── > _ ready ── ░▒▓ ██ ▓▒░ ── $ agent.run() ── ⚡ skill.exec() ── │ ├── tool.chain() │ └── return data ── > _ ready ── ░▒▓ ██ ▓▒░ ── $ agent.run() ── ⚡ skill.exec() ── │ ├── tool.chain() │ └── return data ── > _ ready ── ░▒▓ ██ ▓▒░ ──

│ task.plan() ── ✓ step.complete ── ⟩ memory.store() ── ⚙ config.evolve ── ▓▒░ ░▒▓ ── > await result ── │ task.plan() ── ✓ step.complete ── ⟩ memory.store() ── ⚙ config.evolve ── ▓▒░ ░▒▓ ── > await result ── │ task.plan() ── ✓ step.complete ── ⟩ memory.store() ── ⚙ config.evolve ── ▓▒░ ░▒▓ ── > await result ── │ task.plan() ── ✓ step.complete ── ⟩ memory.store() ── ⚙ config.evolve ── ▓▒░ ░▒▓ ── > await result ── │ task.plan() ── ✓ step.complete ── ⟩ memory.store() ── ⚙ config.evolve ── ▓▒░ ░▒▓ ── > await result ── │ task.plan() ── ✓ step.complete ── ⟩ memory.store() ── ⚙ config.evolve ── ▓▒░ ░▒▓ ── > await result ──

⟩ agent.learn() ── █ obj.persist ── ├── note.save() ── │ skill.improve ── ░░▒▒▓▓██ ── $ deploy --auto ── ⟩ agent.learn() ── █ obj.persist ── ├── note.save() ── │ skill.improve ── ░░▒▒▓▓██ ── $ deploy --auto ── ⟩ agent.learn() ── █ obj.persist ── ├── note.save() ── │ skill.improve ── ░░▒▒▓▓██ ── $ deploy --auto ── ⟩ agent.learn() ── █ obj.persist ── ├── note.save() ── │ skill.improve ── ░░▒▒▓▓██ ── $ deploy --auto ── ⟩ agent.learn() ── █ obj.persist ── ├── note.save() ── │ skill.improve ── ░░▒▒▓▓██ ── $ deploy --auto ── ⟩ agent.learn() ── █ obj.persist ── ├── note.save() ── │ skill.improve ── ░░▒▒▓▓██ ── $ deploy --auto ──

├── inbox.scan() ── ⚡ draft.send ── │ └── crm.update() ── > skill.chain ── ▒▓██▓▒ ── $ observe --all ── ├── inbox.scan() ── ⚡ draft.send ── │ └── crm.update() ── > skill.chain ── ▒▓██▓▒ ── $ observe --all ── ├── inbox.scan() ── ⚡ draft.send ── │ └── crm.update() ── > skill.chain ── ▒▓██▓▒ ── $ observe --all ── ├── inbox.scan() ── ⚡ draft.send ── │ └── crm.update() ── > skill.chain ── ▒▓██▓▒ ── $ observe --all ── ├── inbox.scan() ── ⚡ draft.send ── │ └── crm.update() ── > skill.chain ── ▒▓██▓▒ ── $ observe --all ── ├── inbox.scan() ── ⚡ draft.send ── │ └── crm.update() ── > skill.chain ── ▒▓██▓▒ ── $ observe --all ──

│ review.approve ── ✓ card.move() ── ⟩ report.build ── ⚙ prompt.rewrite ── ░▒▓ ▓▒░ ── > agent.idle ── │ review.approve ── ✓ card.move() ── ⟩ report.build ── ⚙ prompt.rewrite ── ░▒▓ ▓▒░ ── > agent.idle ── │ review.approve ── ✓ card.move() ── ⟩ report.build ── ⚙ prompt.rewrite ── ░▒▓ ▓▒░ ── > agent.idle ── │ review.approve ── ✓ card.move() ── ⟩ report.build ── ⚙ prompt.rewrite ── ░▒▓ ▓▒░ ── > agent.idle ── │ review.approve ── ✓ card.move() ── ⟩ report.build ── ⚙ prompt.rewrite ── ░▒▓ ▓▒░ ── > agent.idle ── │ review.approve ── ✓ card.move() ── ⟩ report.build ── ⚙ prompt.rewrite ── ░▒▓ ▓▒░ ── > agent.idle ──

⟩ knowledge.grow ── █ version.tag ── ├── test.validate ── │ loop.continue ── ██▓▒░░▒▓ ── $ agent.wake ── ⟩ knowledge.grow ── █ version.tag ── ├── test.validate ── │ loop.continue ── ██▓▒░░▒▓ ── $ agent.wake ── ⟩ knowledge.grow ── █ version.tag ── ├── test.validate ── │ loop.continue ── ██▓▒░░▒▓ ── $ agent.wake ── ⟩ knowledge.grow ── █ version.tag ── ├── test.validate ── │ loop.continue ── ██▓▒░░▒▓ ── $ agent.wake ── ⟩ knowledge.grow ── █ version.tag ── ├── test.validate ── │ loop.continue ── ██▓▒░░▒▓ ── $ agent.wake ── ⟩ knowledge.grow ── █ version.tag ── ├── test.validate ── │ loop.continue ── ██▓▒░░▒▓ ── $ agent.wake ──

$ tool.register ── ⚡ auth.oauth() ── │ ├── data.fetch │ └── parse.json ── > _ complete ── ░▒▓██▓▒░ ── $ tool.register ── ⚡ auth.oauth() ── │ ├── data.fetch │ └── parse.json ── > _ complete ── ░▒▓██▓▒░ ── $ tool.register ── ⚡ auth.oauth() ── │ ├── data.fetch │ └── parse.json ── > _ complete ── ░▒▓██▓▒░ ── $ tool.register ── ⚡ auth.oauth() ── │ ├── data.fetch │ └── parse.json ── > _ complete ── ░▒▓██▓▒░ ── $ tool.register ── ⚡ auth.oauth() ── │ ├── data.fetch │ └── parse.json ── > _ complete ── ░▒▓██▓▒░ ── $ tool.register ── ⚡ auth.oauth() ── │ ├── data.fetch │ └── parse.json ── > _ complete ── ░▒▓██▓▒░ ──

│ board.update() ── ✓ task.close ── ⟩ metric.track() ── ⚙ agent.reflect ── ▓▒░ ██ ░▒▓ ── > loop.end ── │ board.update() ── ✓ task.close ── ⟩ metric.track() ── ⚙ agent.reflect ── ▓▒░ ██ ░▒▓ ── > loop.end ── │ board.update() ── ✓ task.close ── ⟩ metric.track() ── ⚙ agent.reflect ── ▓▒░ ██ ░▒▓ ── > loop.end ── │ board.update() ── ✓ task.close ── ⟩ metric.track() ── ⚙ agent.reflect ── ▓▒░ ██ ░▒▓ ── > loop.end ── │ board.update() ── ✓ task.close ── ⟩ metric.track() ── ⚙ agent.reflect ── ▓▒░ ██ ░▒▓ ── > loop.end ── │ board.update() ── ✓ task.close ── ⟩ metric.track() ── ⚙ agent.reflect ── ▓▒░ ██ ░▒▓ ── > loop.end ──

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