A coding agent can produce a large amount of code quickly, but speed does not show whether the right problem was chosen, whether a change fits the existing system or whether the result is safe to release. These technical workshops are for startup founders, product builders and engineering teams that want a controlled agent workflow: from requirements and context to a small prototype, tests, review and a clear implementation decision.
When a technical workshop creates value
- An agent produces code, but tasks keep expanding and fixes introduce new regressions.
- You want to turn a product idea into a small working prototype before committing resources to a full system.
- The team has an existing codebase but does not know which context, constraints and verification commands to give an agent.
- Team members use agents differently, making changes hard to review and work difficult to continue across people.
- A demo works locally, but the team cannot yet explain what is missing for safe deployment, observability and maintenance.
Tangible outputs
What we prepare during the workshop
A bounded technical task
We describe the user need, acceptance criteria, excluded scope, interfaces and main risks so the agent cannot silently redefine the problem.
A project context package
We select the architecture, directory, command, convention and domain information the agent needs before changing code.
A work breakdown
We split the larger goal into small, verifiable changes with explicit stop points and decisions that remain with a person.
A small working prototype
Within the agreed scenario, we build a bounded feature or technical experiment intended to test the most important assumption—not imitate a finished product.
A verification and review flow
We combine automated tests, type and lint checks, diff review, a manual scenario and documentation of failures.
A path beyond the prototype
We identify the security, data, infrastructure, observability, maintenance and ownership work required for a production solution.
Working process
The technical workflow
- 01
Need and constraints
We start with the user scenario, existing architecture, data, integrations and the changes that are deliberately out of scope.
- 02
Context for the agent
We create a concise, traceable task and attach only the rules, files, examples and verification commands it needs.
- 03
Small implementation steps
The agent investigates and proposes a plan first, then changes a bounded part of the system; after each step we review assumptions and the diff.
- 04
Failure and correction
Failures are not hidden: we examine the error, missing context, unsuitable decision and how verification prevents a regression.
- 05
Handover decision
We record what works, what remains unverified and what the team should do next instead of automatically calling a prototype production-ready.
Clear responsibility
A prototype and production are not the same
The agent does not replace technical accountability
A person remains responsible for architecture, access, data, dependencies, code review and release decisions.
The workshop does not guarantee a finished product
The practical scope depends on the starting point and selected task; the aim is to test the workflow and the most important technical assumption.
Production work is scoped separately
Authentication, security, migrations, observability, scale, backups, deployment and support are included only when explicitly agreed as a follow-on phase.
Technical practice example
Breach.gg—from early AI integrations to coding-agent workflows
Code + integrations + verificationBreach.gg first introduced simpler AI integrations in 2022, and I have worked hands-on with AI APIs since 2024. In 2026, I started a separate rebuild phase using coding agents and developed a multi-stage content workflow. I verify the output through diffs, tests and end-to-end scenarios.
View case studyFAQ
Frequently asked questions
What technical prerequisites do participants need?+
Participants should understand the software-delivery workflow, be able to read code and use version control. The exact language or framework is matched to the task; non-technical leaders are better served by the separate AI consultation or talk.
Can we work with our existing codebase?+
Yes, if its structure, run instructions, important rules and permitted access can be reviewed safely before the workshop. If code or data is sensitive, we can use an isolated branch, a prepared extract or a synthetic project.
Which AI agents and development tools will we use?+
Tools are selected around your environment, accounts, data policy and task. The workshop is not tied to one model version; the durable focus is context, task boundaries, verification and a traceable change process.
Will we build a production feature during the workshop?+
The typical goal is a bounded prototype or a clearly verified change, not a promise to release to production on the same day. If the result merits continuation, we separately assess security, tests, migrations, infrastructure, observability and support.
What is the workshop format and price?+
A format may include preparation, a technical session and an output review, but the exact duration, participant count and price depend on the codebase, task and required tailoring. These conditions are agreed before a proposal is provided.
What happens after the workshop?+
The team receives a recorded result, a list of unverified assumptions and a recommended sequence of actions. It can continue independently, request targeted technical consulting or separately scope the work required to turn the prototype into a production solution.