AI agent development
AI agents with a written scope and a person at the gate.
We build agents for the repeatable, verifiable jobs in your product and your operations. Each one gets a policy you approve before it runs, and its output is reviewed before it reaches anything that matters.
Start a project- written policy per agent, approved before it runs
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- sign-offs between an agent and anything that ships
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- agent decisions made in chat history
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Why agents built this way are different
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Bounded by a policy you approve
Every agent runs with a written policy that says what it reads, what it writes and what it must never do. It cannot wander outside its job.
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Inputs in, documents out
Agents do not pass work to each other through conversation. Each one reads a defined input and writes a defined output that a person can inspect.
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Reviewed, every time
An agent output is a result attached to a piece of work. A person reads it, rejects it or approves it before it goes anywhere.
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Replayable
Because inputs and outputs are recorded, an agent can be re-run with a different model or policy and the results compared.
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Runs where your data is
Agents run in your cloud accounts, against your data, under your access controls.
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Audited by default
Every agent run is logged with what it was given, what it produced and who reviewed it. Compliance questions have answers.
When you are ready for agents
Agents pay off on work that is repeatable, verifiable and bounded. These are the signs you have it.
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The same research is done every time
Requirements gathering, competitor checks, data pulls and summaries that follow the same shape for every request.
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Drafts wait on a busy person
PRDs, design notes, test plans and documentation sit in a queue when a first draft could be produced in minutes and reviewed in ten.
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Classification and routing are manual
Tickets, documents, leads or requests are read by a person only to decide where they go.
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You tried a chatbot and could not trust it
Outputs were plausible, unbounded and unrecorded. You need the opposite: narrow jobs with reviewable results.
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Compliance asks what the AI did
You need to show which model did what, with which inputs, approved by whom.
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Your developers already use AI, invisibly
The work is happening; the record is not. Governance has to catch up with practice.
Do two or more of these apply? Tell us which ones and we reply with how we would run it.
Agents we build
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Product agents
Agents embedded in your application that classify, draft, extract, summarize and act, with their work recorded.
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Operations agents
Routing, triage, monitoring and reporting jobs that run on a schedule or on an event and write down what they did.
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Delivery agents
Research, requirements, design and QA agents that do the repeatable work inside a software project, between human approvals.
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Policies
The written scope for each agent: what it knows, what it may touch, what it must never do.
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Evaluation and replay
Harnesses that re-run recorded inputs against new models or policies and report the difference.
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Human gates
The approval and review steps that put a person between an agent and anything that matters.
How an agent engagement runs
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Discovery
We map the jobs that are repeatable and verifiable, and the ones that are not. The ones that are become the first feature requests.
In the record Feature request per agent job, success measures
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Policy design
For each agent we write what it reads, what it writes, and what it may never do. You approve the policy before any agent runs.
In the record Agent policy, design package
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Design approval
A human gate. The policy is the scope, and the work waits for your approval of it.
In the record Logged approval
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Build and bound
The agent is implemented with its inputs and outputs typed and recorded, inside your accounts.
In the record Implementation package
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Automated QA and manual review
Recorded inputs are replayed through the agent. QA writes a report; a person reviews outputs at the second gate.
In the record QA report, review report
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Release and watch
The agent goes live inside your product or operations. Every run is logged, and drift shows up in the record.
In the record Release candidate, run log
Bounded agents against the alternatives
| Item | Chat assistant | Agent framework | Sydeso agents |
|---|---|---|---|
| Scope | Whatever the prompt says | Whatever the code allows | A written policy, approved by you |
| Memory | Conversation history | Vector store or scratchpad | Recorded inputs and outputs |
| Review | Read the chat | Read the logs | A human gate with the output attached |
| Audit | None | If you build it | Every run, with what it was given and who reviewed it |
| Replay | No | Partial | Re-run recorded inputs against a new model or policy |
| Where it runs | Vendor cloud | Your choice | Your accounts, your data |
How an engagement is structured.
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Week one
Discovery
A week with the people who own the problem. We write the feature request, the PRD and the design package, and agree what done means.
- Ends in a PRD and a design package you own
- You approve it before anything is built
- Comes with a price for the build
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After approval
Build
Development in short cycles against the approved scope. Automated QA writes a report on every release candidate and a person reviews it before it ships.
- Priced per project against the PRD
- Changes are new feature requests, priced the same way
- Every document is yours as it is produced
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After launch
Operate
We deploy it, monitor it and run releases, month to month. Production is where the next feature request comes from.
- Monthly, separate from the build
- Take it in-house whenever you are ready
- The record comes with it
Questions
- Which models do you use?
- The ones that fit the job and your policy. Model choice is part of the design package you approve, and the record shows which model ran each job, so switching later is a re-run, not a rewrite.
- Can agents run on our infrastructure?
- Yes. Agents run in your cloud accounts, against your data, under your access controls.
- How do you stop an agent doing something it should not?
- The policy bounds what each agent may read and write, and two human sign-offs sit between agent output and anything that ships. Every run is logged with its inputs and its reviewer.
- Do we need a whole platform to use one agent?
- No. An agent is a piece of software in your product or your operations, built and run like any other. The platform that runs this process for your own team is in development; join the waitlist on the platform page if that is what you want.
- How quickly do you reply?
- By email, with how we would run the work, not a call to book a call. Send one paragraph about what should ship first and you get a written answer.
- Where does the work live?
- In your accounts. Code in your repository, infrastructure in your cloud, documents where your team can read them. We bring the process, not a dependency.
- What happens to the work if we stop?
- You keep everything: code, infrastructure, requirements, design packages, QA and review reports. There is no handover because none of it was ever ours.
Name the job.
Tell us the repeatable work you want an agent to do and we reply with the policy and the gates we would put around it.
Start a project