Product engineering
Agents clear the backlog of small, well-specified tickets so engineers stay on the work that needs design judgement.
3.1× more tickets closed
Your AI workforce
Agents for every kind of work
Build, deploy and supervise AI agents that write software, run test suites, train models, handle operations and talk to your customers. One platform, every role.
Trusted by businesses, engineering and operations teams
Supporting partners
Where the hours go
0% of skilled work is repetitive enough to delegate
Writing boilerplate, chasing flaky tests, retraining a model on fresh data, triaging the same ticket for the hundredth time. Agents take the repeatable part so your people keep the judgement calls.
0/7
Always working
0+
Tools and APIs connected
0s
Of agents running in parallel
Software engineering
Agents that write and ship code
Give an agent a repository and an issue. It reads the codebase, plans the change, writes it in your conventions, and opens a pull request a human can actually review.
- Works inside your existing repo and CI
- Follows the patterns already in your code
- Every change arrives as a reviewable PR
coding-agent · repo/checkout
read 42 files · found 3 call sitesdrafted plan · 2 files to changewrote src/checkout/retry.tsupdated src/checkout/index.tsran lint + typecheck · cleanopened PR #412 · awaiting reviewTesting and QA
Agents that keep the suite green
Generate missing test coverage, reproduce reported bugs, and triage failures the moment CI goes red — including the flaky ones nobody wants to chase.
- Writes tests against real usage, not guesses
- Reproduces and isolates flaky failures
- Proposes the fix alongside the diagnosis
qa-agent · fix/flaky-upload
145 passedwas 3 failing
async function uploadAsset(file: File) { return await put(file) return await retry(() => put(file), { attempts: 3, backoff: 'exponential', })}Reproduced 40× locally before proposing the patch.
ML and data
Agents that run your pipelines
Schedule retraining, watch for drift, run evaluation suites and keep feature pipelines healthy — with every run logged, scored and reproducible.
- Drift detection and automatic retraining
- Evaluation suites on every model version
- Full lineage from raw data to deployed model
ml-agent · nightly-retrain
ingest1.2M rows
features148 cols
trainepoch 12/12
evaluateF1 0.94 ▲ 0.02
deploycanary 10%
Triggered by drift on conv_rate — no human in the loop.
Voice and phone
Agents that answer the phone
Natural-sounding voice agents take inbound calls and make outbound ones — booking appointments, qualifying leads and following up, in your brand's voice, around the clock.
- Picks up every call in under a second
- Books and reschedules straight into your calendar
- Hands off to a person with a full call summary
voice-agent · inbound line
+1 (555) 014-2290
Existing customer
Hi — can I move my appointment to next week?
Of course. I have Tuesday at 10:30 or Thursday at 2:00.
Tuesday works.
Done — you're booked for Tuesday 10:30. I've texted you a confirmation.
Appointment moved · calendar updated · no hold music
Customer-facing
Agents your customers talk to
The same platform runs the front of house: lifelike voice agents, digital human avatars and messaging agents that book, track and resolve around the clock.
- Real-time voice with sub-second latency
- Brand-matched digital human avatars
- Warm hand-off to a person with full context
You
Who runs agents here
Every team has work worth delegating
The same platform, pointed at whatever your team spends its week doing.
Data and ML
Retraining, evaluation and pipeline babysitting run themselves, with drift alerts before a model quietly degrades in production.
80% less pipeline toil
Customer operations
Voice, chat and avatar agents answer instantly at any hour, escalating to a person the moment a conversation needs one.
38% fewer escalations
The difference
What changes when agents do the repeatable work
Without Myndboosters
- Small tickets sit in the backlog for weeks
- Flaky tests get muted instead of fixed
- Retraining happens when someone remembers
- Every new hire needs months of context
- Work stops when the team logs off
- No record of why a decision was made
With Myndboosters
- Well-specified work ships the same day
- Failures are reproduced, diagnosed and patched
- Pipelines retrain on drift, not on memory
- Agents onboard from your docs in an afternoon
- Thousands of tasks run in parallel, overnight
- Every action traced, scored and reversible
How it works
How easy is it to set up?
Five steps from signing up to a working agent. Most teams have one running the same day.
- 1
Ground your agent
Point it at your repo, docs, data warehouse or help centre. It reads everything and builds its own working knowledge.
- 2
Give it tools
Connect the systems it needs — GitHub, CI, your warehouse, your ticketing system, or any API.
- 3
Set the goals
Define what done looks like: a merged PR, a green suite, a retrained model, a resolved ticket.
- 4
Deploy and watch
Run it on a schedule, a webhook or a queue, with every step traced as it happens.
- 5
Adapt and improve
Correct it once and the agent applies that correction everywhere, permanently.
Safeguards and control
Myndboosters does it all
The controls that make an autonomous agent safe to put near production.
Tool use and integrations
Agents call your APIs, run shell commands, query warehouses and open pull requests — scoped to exactly the permissions you grant.
Full observability
Every step, tool call and token traced, scored and replayable.
Knowledge integration
Repos, Notion, Drive, Zendesk or a raw sitemap, synced on your schedule.
Guardrails that hold
Scope every agent to approved tools and repos, require human approval on sensitive actions, and roll back anything an agent did.
Questions
Frequently asked questions
Get started
Ready to put your agents to work?
Launch your first agent today and hand it the work your team never has time for.