AI agents & MCP
Agents build beside your engineers.
Assign tasks to AI agents as you would to a colleague. They create data, comment, move workflows forward and hand work to other agents, within the rights you give them. Nothing is merged without an engineer's approval.
- −50%
- time spent on repetitive engineering tasks
- −30%
- time to market
Task
The bracket moves to supplier B: +180 g. Update the mass budget, flag what is at risk and draft the change request.
Plan
- Read the BOM and the bracket's links
- Run the mass roll-up in Python: arm 4.82 → 5.00 kg
- Flag REQ-112 (mass budget) and 3 tests to rerun
- Open branch auto/bracket-b and draft CR-118
Output
- Arm assembly mass4.82 → 5.00 kg
- REQ-112 mass budgetmargin −2 %
- Tests to rerun3
- Evidenceattached
01
Skills, automations and AI engineers.
Three building blocks turn the routine checks of your team into work that agents do for you. Engineers spend half as much time on repetitive tasks and keep their week for design decisions.
Skills
Skills describe your standards and team conventions in plain language: naming rules, review criteria, clauses of a standard. Agents apply them to every item they create or check.
Automations
A pull request, a CAD revision or a new supplier PDF starts the agent workflow you assigned to it. The checks your process requires run on every change, without anyone launching them.
AI engineers
Agents named after an engineering job take the tasks your team assigns them: tracing dependencies, checking test coverage, updating roll-ups and drafting change requests.
02
A team of AI engineers, one per engineering job.
You assign tasks to each agent as to a team member. It applies your skills, can hand part of the work to another agent, and submits the result to an engineer for approval.
AI engineering system assistant
Shows the parts, requirements and tests a change affects before you approve it.
AI mechanical engineer
Keeps the mass and cost roll-ups of the BOM up to date after every change.
AI requirements engineer
Links each requirement to the design element and the test that prove it, and flags the gaps.
AI verification engineer
Lists the tests a change invalidates and reopens them for a rerun.
AI release engineer
Prepares the baseline and the evidence for each design review gate.
Your own agent
Runs your own instructions with your skills and the rights of its service account.
03
Ask what if. Get the full analysis.
- 1
Ask in plain words
What if the bracket moves to supplier B?
- 2
The agent works on a branch
It follows the links from the bracket across the system and computes the answer on a branch, without touching the baseline.
- 3
A decision report comes back
The report lists the affected items, the changes they need and whether the requirements still hold.
- 4
Before and after, side by side
You compare both versions, mass and cost included, and decide.
- Arm assembly mass4.82 kg5.00 kg
- Mass budget margin+1.6 %−2.0 %
- Requirements affected01
- Tests to rerun03
- Variants touched02
04
Every change is checked by an agent.
When a change comes in, the agent finds the parts, requirements and tests it affects, proposes the updates on a branch and notifies the reviewers who must approve them.
01
A change comes in
02
The agent finds what it affects
03
Fixes proposed on branches
04
Engineers notified, the decision stays human
05
Agents take the tasks you assign. Engineers approve their work.
You assign the task
You assign it like any task, or a trigger does it for you: a pull request, a supplier PDF or an updated standard.
The agent works on a branch
It creates the missing items, comments on what it found, moves the workflow forward and hands sub-tasks to other agents, without touching the baseline.
An engineer reviews and merges
The branch is merged into the baseline only after a reviewer approves it, like a pull request.
06
Write your engineering standards once.
Plain instructions, no code
You write naming rules, quality criteria and the clauses of your standards as plain text.
Reused by every agent
Every agent you run applies the same rules, the same way, on every item.
Combined in workflows
A compliance check can apply your verification rules and your naming rules in a single run.
AI verification engineer
Agent settings · Koddex
- Structure a customer specification
- Allocate requirements to the system
- Write test skeletons from requirements
- Run impact analysis on every change
- Draft change requests for review
07
Bring your own agent.
Connect Claude, ChatGPT or your own agent to Koddex through MCP. It reads and writes your engineering data with the rights of the account that connects it, and every action is logged.
08
Also built into Koddex agents.
Sandboxed Python
Agents run mass roll-ups, tolerance stack-ups and unit conversions in Python, in an isolated sandbox. The results feed the agent's reasoning and appear in its transcript.
Branches and merge
Each agent fix lives on its own branch, which you compare with the baseline and merge only after approval.
Values computed by Koddex
Mass, cost and coverage roll-ups are computed by Koddex. The agent reads these values and never makes them up.
Service accounts with access tags
Each agent connects with a service account whose access tags decide what it can see and edit, down to a single item.
Every action on the activity
Each action is recorded on the item's activity: who, when and with which data. You review it like any other change.
The model you choose
Connect Claude, ChatGPT or your own agent to Koddex through MCP.
A 20-min demo. Your first use case live in 1 month.
Bring one question your team asks every week. In the demo, we ask a Koddex agent the same kind of question on a product like yours.