01
Context
An agent performs according to what it knows. We teach your team to give it rules, examples, and the relevant files instead of loose prompts.
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For the human team
Agents write the code. Your team decides what reaches production.
We train your developers to delegate to AI agents with a method: what to ask for, how to review the result, and when not to trust it.
What your team learns
Delegating tasks to agents
Reviewing generated changes
Verifying before merging
Protecting code and data
A new craft
Programming with agents is a different craft.
With agents, most of the typing goes away. What remains is harder: describing the task well, splitting it into steps you can check, and judging whether the result is right. A team that doesn't practice this writes more code without building better software.
What changes for your team
The value moves from writing code to deciding which code deserves to exist.
The issance method
Four habits for working well with agents.
Tools are half the job. The other half is how your team uses them, and that can be trained too.
02
Delegation
Which tasks to hand an agent, how big, and with what definition of done, so it comes back with something useful.
03
Review
How to read a generated change, spot what doesn't belong, and ask for specific fixes.
04
Verification
No change counts as done until the tests pass on the real product. What the agent says it did is not evidence.
Human-in-the-loop
The agent executes. A person owns the result.
Every task has a human owner. The agent moves fast inside the limits that owner sets and hands the decision back when something is unclear.
- Permissions per tool and repository
- Human review before every merge
- A record of what each agent did
- Cost limits per team
More hands don't mean less judgment. Every change still has a person behind it.
Method
From the first session to a self-sufficient team.
01
Assess
We look at how your team works today and where an agent would save the most time.
02
Install
We leave tools, models, and rules configured on every machine.
03
Practice
Hands-on sessions on real tasks from your repositories.
04
Review
We go through the first agent-made changes together and adjust the method.
05
Support
We update the stack and the practices when something better comes out.
What changes
More work done, with the same control.
01
Delegated tasks
Your team knows which parts of its work it can hand to an agent.
02
Review with judgment
Generated changes are read and corrected before they reach production.
03
Shared knowledge
Rules and workflows live in the repository, written down for the whole team.
04
Confidence
The doubt about whether you're making the most of AI, or falling behind, goes away.
Assessment
Find out how much your team can delegate to agents.
We review your stack, your repositories, and how you use AI today, and propose a concrete next step.
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