OCTOBUS BY AGEN-IT · PROFILE-DRIVEN AI CO-PILOT · EARLY ACCESS OPEN

Any profile. Any stack.
Hardware in the loop.

Octobus remembers your codebase, walks the work from requirements to a tested PR, and proves it on real hardware — for embedded, aerospace, IoT, and any software project. Runs on the LLM you already pay for. Your source code never leaves your laptop.

Get started Documentation

Hardware in the Loop

Embedded & IoT

Aerospace DO-178C

AI Models & LLMs

Test Automation

Compliance/Audit

Local Agents

Marketplace

$ npm install -g @agenit/cli · works with your existing LLM auth · free during early access

Used by teams building on

ClaudeGeminiOpenAIOllamaVS CodeMCP

EmbeddedAerospaceIoTAutomotiveWeb

V-Model stages

LLM backends

Server assets

Monorepo pkgs

It remembers.

Your standards, decisions, and naming — recalled on every session.

It ships.

Idea to a tested PR, walked stage by stage with approval gates you control.

On real hardware.

J-Link · CAN/LIN · Saleae — flashed, captured, and decoded in the loop.

Your code stays yours.

Local-first by default. Your LLM, your machine, your contract.

Hardware in the loop

From silicon to a tested PR — without leaving the bench

Octobus speaks to the same probes on your desk. It flashes the board, streams RTT logs, decodes the CAN bus, captures the logic analyzer — then writes the fix, the test, and the traceability that proves it works on real hardware, not just in a chat window.

HIL signal coverage

96%

decoded

Of bus + log events on the bench are captured, decoded, and linked to a test — automatically.

Signals it speaks

CAN / LIN frames100%

RTT log lines100%

GPIO / SPI edges98%

Bench → tested PR

30m↓ from 8h

Reproduce on hardware, fix MISRA-clean, add the HIL regression, and ship the evidence pack — in one uninterrupted run.

Illustrative · pilot teams in automotive embedded.

In plain English

What it actually does

You already use an AI. Octobus is the layer around it that makes it useful for real software — the kind that ships, gets tested, and ends up in production.

🧠

Remembers your code

Not a chat. It writes down your style, your decisions, and the names you use, and reads them back into every session. No more re-explaining.

✅

Finishes the job

Walks Plan → Code → Tests → Audit. Stops at approval gates you can move through one click, or auto-approve when the change is small.

🔌

Brings your LLM

Use the one you pay for: Claude, Gemini, GPT-4, or a local Ollama box. Same workflow, same skills — just point at your endpoint.

🔒

Local-first by default

Your source never leaves your laptop. Only the AI's instructions stream in, get used, and get wiped when you're done.

🤝

Squad of helpers, not one chat

Cheap helper models read the codebase in parallel; one capable model writes the answer with that context already in hand. Faster and cheaper.

🛠

Works on what you build

Embedded, Aerospace, IoT, Automotive, Web — switch profile, same product. Hardware engineers get J-Link / CAN / Saleae / GDB built in.

/ How it's different

vs the AI tools you've already tried

Capability	Copilot	Cursor	Claude Code	Octobus
Memory across sessions	—	limited	session	plaintext markdown · git-diffable
Bring your own LLM	OpenAI	few	Anthropic	6 backends incl. Ollama
Code stays on your machine	no	no	yes	yes · plus IP wipe on exit
Finishes the job (Plan→Test→Audit)	—	—	manual	/run walks it
Approval gates	—	—	—	configurable per stage
Hardware (J-Link / CAN / Saleae)	—	—	—	first-class
Open & inspectable	no	no	partial	open runner · signed assets

We're not claiming Octobus replaces these — many users run it alongside Cursor or Claude Code.

Tell it what you want

“build me a CAN driver” or “add OAuth to the signup page”

›

It plans

writes requirements + decisions you can edit

›

It codes

tags every change against a requirement so nothing is orphaned

›

It tests

writes the tests it would want a junior to write

›

It hands you the PR

with the audit trail. You hit Approve.

Stage 01 · The hurt

Sound familiar?

Most engineering teams already have an LLM. What they don't have is the discipline, memory, and integrations to make it pay back. Here's what Octobus removes from the week.

Where the week goes

% of an engineer's week — before vs with Octobus

beforewith Octobus

Re-explaining context

18%→1%

Traceability & audit

22%→3%

Tool switching

14%→2%

Boilerplate code

16%→4%

Test scaffolding

12%→3%

Actual engineering

18%→87%

Illustrative · based on pilot teams across embedded, aerospace, IoT, and regulated SaaS.

“I keep re-explaining our stack to the LLM.”

→Soul + project memory remember your standards, decisions, and naming on every session.

“Traceability is a part-time job nobody wants.”

→Every requirement, file, and test is linked automatically. /audit produces the matrix on demand.

“Our LLM lives in a chat window, not in our process.”

→REPL + V-Model state machine + approval gates turn the model into a teammate, not a toy.

“We can't send proprietary code to a public API.”

→Bring your own LLM (Gemini, Claude, on-prem, vLLM). Memory + code stay on your machine.

“Our toolchain is fragmented — DOORS here, Jira there, J-Link on a lab PC.”

→MCP servers + skills bridge ALM, debuggers, logic analyzers, CI, and chat into one shell.

“Agents work for 20 minutes then forget the project.”

→File-locked markdown memory + decay/graph keep context durable across days and squad members.

Stage 02 · A day in the life

02:14 AM — pager goes off.

Same incident, two worlds. The example below is from an embedded team — but the shape is identical for SaaS: a stale on-call, a ticket nobody can root-cause without waking three more people, a fix that lands without tests. Octobus wakes the squad instead of the team.

embedded systemsalso fits → aerospace · IoT · SaaS on-call · platform infra

/ Without Octobus

elapsed: 6h 42m

The pager war-room

02:14
PagerDuty
Pages on-call. On-call is the firmware lead — wrong domain.
02:31
On-call
Wakes the SW architect to triage. Architect needs the requirements engineer to confirm spec intent.
03:08
Architect
Wakes the test lead — last regression run was on a stale branch. Lead can't reach the J-Link rig in the lab.
03:55
Test lead
Pulls the integration engineer out of bed to forward the Saleae trace from the lab PC.
04:40
All four
Stuck waiting on each other in a Teams call. Nobody has the audit trail to know what changed since v2.3.1.
06:20
Manager
Joins to escalate. Asks for traceability matrix that hasn't been updated since last sprint.
08:56
Patch
Lands without tests. Compliance evidence comes Monday — maybe.

engineers woken

tests added

blamey postmortem

/ With Octobus Mission Squad

elapsed: 18m

The squad wakes — humans sleep

02:14
webhook → /goal
Ticket lands. Octobus spins up the squad with the failing trace, recent commits, and matching SWR.
02:15
codebase-mapper
Phase 1 · scans the repo, identifies the affected component and its @req tags.
02:16
dependency-grapher
Phase 1 · pulls the call graph and decisions.md entries for the surrounding subsystem.
02:18
test-gap-scanner
Phase 2 · finds the missing regression in tests/integration — that's why this slipped.
02:21
implementer (planner)
Phase 3 · drafts the fix on a worktree, tagged @req SWR-014, ADR added to decisions.md.
02:26
/swe5
Regenerates unit + integration tests, runs them against the J-Link rig — all green.
02:30
/swe6
Regenerates traceability.json, attaches the evidence packet, opens a PR awaiting one human approval.
07:45
engineer
Wakes up, reviews diff + traceability over coffee, hits Approve. Done.

engineers woken

100%

trace + tests included

approval, then merged

The founding decision · Local-first IP

Your code stays on your laptop.
The intelligence comes to you.

Most AI dev tools want your codebase on their servers. Fine for a side project — not fine if you work on anything proprietary. Octobus flips it: the tool runs on your machine, the AI's instructions come to you, and they're cleared from disk when you're done. The wire only carries your licence going out and the AI's skills coming in. Your source code? Never moves.

/ Local-first data flow

your code never moves

The wire only carries a quick licence check going out, and AI playbooks coming in. Your code, prompts, and memory never leave your laptop.

Open & inspectable

The runner is open and auditable. You can lock it to a specific release and verify the signature.

Just-in-time delivery

AI playbooks arrive when a session needs them and never sit on your disk between runs.

Cleared when you're done

Anything we deliver is removed from your machine the moment you close the session.

Air-gap option

Point at a local model (Ollama) and a local cache and run fully offline. Nothing leaves your network.

Stage 03 · Directional numbers

What pilot teams are seeing

Three lenses on the same shift: time-to-resolve, what ships with each release, and how much throughput a squad gains in a sprint. These are directional averages from early-access teams — not a guarantee, not a marketing number.

Mean Time to Resolve

Minutes per stage of an incident · lower is better

Total: 8h 0m → 30m

Triage

95m→6m

Reproduce on HW

70m→4m

Fix + MISRA-clean

110m→12m

Tests + HIL regression

75m→5m

Evidence + audit pack

130m→3m

Release evidence packet

What Octobus auto-generates for every audit

95%

auto

Traceability matrix100%
MISRA / OWASP report100%
Test coverage XML95%
Change log + diffs100%
Risk + impact note80%
Reviewer narrativemanual

Team throughput

Per squad, per sprint · pilot averages

Tickets / week

17↑ 325%

was 4

Lead time (h)

6h↑ 84%

was 38h

Coverage (%)

89%↑ 65%

was 54%

Approval gates

7↑ 250%

was 2

Directional · averaged across early-access teams in embedded, aerospace, IoT, and regulated SaaS. Detailed pilot report available under NDA.

Stage 04 · What makes it work

Built for real engineering

Eight pillars that turn an LLM into a disciplined engineering teammate — not a chat window.

Memory

Profile-Driven Co-Pilot

Switch profiles, not tools. Embedded, Aerospace, IoT, Automotive, Web — one tool purpose-built per discipline. Onboards once, remembers your stack, standards, and decisions across every session.

Process

Standards-Native V-Model

ASPICE SWE.1–6, DO-178C DAL A–D, IEC 61508, IEC 62304, OWASP — enforced per your active profile. Five stages, six commands (/swe1–/swe6), one /run that walks them end-to-end with configurable approval gates.

Backends

Bring Your Own LLM

Six backends: Gemini CLI (default, free OAuth), Claude CLI, Antigravity (agy), Anthropic SDK, OpenAI, and Ollama for air-gapped. Octobus picks per config — no vendor lock-in.

Agents

Mission Squad

Parallel helpers in phases — research (codebase-mapper, pattern-finder, dependency-grapher) → critique (security-reviewer, test-gap-scanner, complexity-grader) → synthesis. Workers on cheap models, planner on capable.

Hardware

Hardware-in-the-Loop

First-class J-Link, CAN/LIN, Saleae Logic 2, OpenOCD, and GDB integration. Arduino and ESP32 as HIL nodes. Supports Zephyr RTOS, FreeRTOS, and bare-metal targets.

Extensibility

Octobus Tool Suite

Unified marketplace for MCP servers, skills, profiles, and recipes — all developed by Agen-IT. Hardware bridges, safety scanners, ALM connectors, and code intelligence tools. Compose your toolchain without forking the CLI.

Spec

Spec-Driven Development

Nine-phase SpecKit pipeline takes you from constitution to issues, with PMO standards enforcement and budget tracking built in.

Autonomous

Overnight Autonomy

/overnight runs a git-backed Plan → Code → Test → Evaluate loop with worktree parallelism and per-iteration rollback safety.

Stage 05 · Plan → Code → Tests → Audit

From idea to a finished PR

Most AI tools stop after "here's some code". Octobus keeps going — it writes down what you wanted, decides how, codes it, tests it, and checks that everything ties back. Each step writes a real file in your repo and pauses for a quick Approve.

Under the hood it's the ASPICE V-Model — the same workflow safety-critical embedded, aerospace, and IoT teams use. You don't have to care about that to use it.

Stage 1requirements.md

/swe1

Requirements

Extracts an SWR-NNN registry from your spec — every item gets a stable ID you can trace later.

Stage 2decisions.md

/swe2

Architecture

Records ADR-style decisions for components, interfaces, and trade-offs you commit to.

Stage 2decisions.md

/swe3

Detailed design

Drills into component design and refines the same decisions.md with module-level intent.

Stage 3source + @req

/swe4

Implementation

Writes code tagged @req SWR-NNN against your requirements — traceability happens at write-time.

Stage 4tests/

/swe5

Testing

BDD scenarios, unit tests, integration tests — generated per profile (embedded, web, mixed).

Stage 5traceability.json

/swe6

Audit

Regenerates the traceability graph: every SWR → file → test, with gap report and evidence packet.

Stage 06 · Fits your toolchain

The Octobus Tool Suite — one shell for the whole stack

ALM (Codebeamer, DOORS, Polarion, Jama), PM (Jira, ADO), CI (Jenkins, GitLab), debuggers (J-Link, Lauterbach, OpenOCD), bus & logic (Saleae, PEAK, Vector), IDEs, and chat — all reachable from one REPL via MCP servers and skills.

● Native MCP
● ReqIF round-trip
● CI webhooks
● HW Python bridge

CodebeamerALM

REST + traceability sync

IBM DOORSALM

ReqIF import / export

PolarionALM

Work-item bridge

Jama ConnectALM

JiraPM

MCP + webhooks

Azure DevOpsPM

JenkinsCI

GitLab CICI

SEGGER J-LinkDebug

Flash + RTT

L32

Lauterbach TRACE32Debug

OpenOCDDebug

Saleae Logic 2HW

Capture + decode

PEAK CANHW

CAN / LIN

Vector CANoeHW

VS CodeIDE

Skills + REPL panel

JetBrainsIDE

NeovimIDE

SlackComms

Job notifications

MS TeamsComms

ConfluenceDocs

NotionDocs

MCP

S3 / MinIOData

CodebeamerALM

REST + traceability sync

IBM DOORSALM

ReqIF import / export

PolarionALM

Work-item bridge

Jama ConnectALM

JiraPM

MCP + webhooks

Azure DevOpsPM

JenkinsCI

GitLab CICI

SEGGER J-LinkDebug

Flash + RTT

L32

Lauterbach TRACE32Debug

OpenOCDDebug

Saleae Logic 2HW

Capture + decode

PEAK CANHW

CAN / LIN

Vector CANoeHW

VS CodeIDE

Skills + REPL panel

JetBrainsIDE

NeovimIDE

SlackComms

Job notifications

MS TeamsComms

ConfluenceDocs

NotionDocs

MCP

S3 / MinIOData

Stage 06b · Your LLM, your auth

Six backends. Zero lock-in.

Already pay for Claude? Use it. Have a Google Workspace account? Free tier works. Air-gapped lab with an Ollama box? That works too. Flip one line in agenit.toml — the V-Model, the squad, and every skill keep working.

gemini-clidefault

OAuth · Google

Free tier plus Pro/Ultra Google accounts. Spawns gemini -p ... as a subprocess. No API key needed.

claude-clisubscription

OAuth · Claude.ai

Reuses your Claude.ai Pro / Max / Team / Enterprise login. Trusts the CLI's built-in tools.

antigravity-clinext-gen

agy · OAuth or API

Google's successor to gemini-cli after 2026-06-18. Skills folder layout: .agents/skills/*.md.

anthropic-sdkAPI key

Messages API

Direct API path for headless CI and machines without the claude binary. Set ANTHROPIC_API_KEY.

openaiAPI key

Chat Completions

Cross-provider parity. Useful for benchmarking the same prompt across models.

ollamaair-gapped

Local HTTP

Zero outbound traffic. Pair with the local orchestration cache for a fully offline install — no licence check on each run.

// agenit.toml

[backend]
provider = "claude-cli"      # gemini-cli · claude-cli · antigravity-cli
                              # anthropic-sdk · openai · ollama
worker_model  = "haiku"      # squad helpers — cheap tier
planner_model = "sonnet"     # primary agent — capable tier

[squad]
auto_squad = true
max_concurrent_agents = 4

Stage 07 · Under the hood

Three layers. Clear contracts.

A stateful TypeScript CLI drives your chosen LLM, which delegates to a Python bridge for hardware and heavy parsers.

Layer 1

Octobus CLI

Runs on your machine

▸REPL, slash commands, autocomplete, history
▸V-Model workflow with approval gates
▸Local persona + project memory
▸Parallel helper agents · autonomous goal loop
▸Local code search

Layer 2

Your LLM

Your choice · your contract

▸Skill auto-activation by intent
▸Standard tools: read, write, shell
▸Pre/post hooks you control
▸MCP server bridging

Layer 3

Hardware & docs bridge

Python sidecar

▸JLink flashing + RTT log streaming
▸CAN / LIN bus capture + decode
▸Saleae Logic 2 captures
▸PDF / Excel / Word ingest

Stage 08 · Try it

Touch the REPL

A mock of the Octobus REPL so you can feel how it works before installing. Type a slash command or anything in free form — outputs are simulated, but the flow is the real one.

▸ Mock REPL wired to the headline slash commands
▸ History with ↑/↓ · clear with ⌃L
▸ Free-form prompts route to the best skill in the real REPL

demo.mp4 · 1:021080p · captions

Get the real CLI · Request early access →

agenIT·flow[*demo-project]

YOLO yoloBACKEND geminiMODEL gemini-2.5-proPOS 8/8SERVER ●

* SYSTEM

Octobus by Agen-IT — profile-driven AI dev co-pilot.

Type a question, or /help for commands.

[demo] flow>

try:↑/↓ history · ⌃L clear

Stage 09 · The vocabulary

92+ slash commands

A REPL designed like an OS shell — every workflow, integration, and persona is one command away.

V-Model

/run/swe1/swe2/swe3/swe4/swe5/swe6

Autonomous

/goal/squad/agent/jobs

Memory

/memory/soul/global/graph

Backends

/backend gemini-cli/backend claude-cli/backend ollama/backend openai

Hardware

/jlink/can/logic2/gdb

Tools & MCP

/mcp/skill/recipe

Stage 10 · PMO & budget control

Sprints, budgets, and PMO standards — built in

Octobus isn't just a code co-pilot. It ships with a Project Management Office layer that plans sprints, tracks budgets, enforces standards on every activation, and reports back without anyone opening a spreadsheet.

Sprint planning

Plan, slice, and assign

/sprint plan turns a goal into stories, slices them into squad-sized tasks, and assigns each to the right agent — with story points and dependencies.

Budget control

Token + time budgets

Every goal carries a turn budget and an LLM-token budget. /goal status shows burn-down per stage; Octobus stops at the gate when budget is depleted.

PMO standards

Standards on activation

PMO templates (naming, commit style, doc layout, branch policy, license header) are loaded with the profile and enforced via pre-tool policy hooks.

Sprint review

Automatic review packet

/sprint review compiles velocity, burn-down, requirement coverage, MISRA findings, and per-agent metrics into a single markdown report.

Risk + impact

Risk before merge

/risk runs an impact analysis against the traceability graph and flags downstream tests and requirements affected by the change.

Reporting

Status without spreadsheets

/report rolls up tasks, approvals, decisions, and budget across projects — exportable to Confluence, Notion, or your SIEM.

/ Sprint burn-down

Sprint 14 · 10 working days · 84 story points planned

on track

/ Goal budget gauges

GOAL-20260527003238-d53f · DIO Driver for S32k118

Turn budget

18 / 50 turns

Token budget

412k / 800k tokens

Time budget

41m / 2h wall time

Approval gates

4 / 7 cleared

✓ all budgets nominal · /goal continue

Stage 11 · Marketplace

A platform, not a binary

Five flavours of extension — Skills (what the AI can do), Plugins (where it lives — VS Code, JetBrains, Slack, CI), Tools (MCP servers for your toolchain), Profiles (full preset bundles), and Recipes (one-shot prompt chains). Install with a slash command. Publish your own.

Skills

misra-c-2012 · owasp-asvs-audit · tdd-loop

browse →

Plugins

vscode-repl · slack-jobs · github-actions

browse →

Tools

codebeamer-mcp · jlink-bridge · jira-issues

browse →

Profiles

aerospace-do178c · iot-zephyr · automotive-ecu

browse →

Recipes

migrate-doors-to-ears · rewrite-as-tdd

browse →

Browse the marketplace →Publish your own ↗

92 server-side assets in production · open submission queue

Stage 10 · For decision makers

Bring your LLM into your world.
Not the other way around.

You already pay for an LLM. The added value isn't more tokens — it's orchestration, memory, governance, and integration that turn the model into an accountable engineer inside your existing process.

BYO

Bring Your Own LLM

Plug in Gemini, Claude, GPT-4.1, Azure OpenAI, AWS Bedrock, on-prem vLLM or llama.cpp. Same skills, same REPL, your endpoint.

VPC

Data sovereignty

Memory, code, and prompts stay in your VPC or on the developer's machine. No telemetry, no training-on-your-data clauses.

GOV

Skill governance

Sign and pin skills, MCP servers, and extensions. PMO templates enforce coding standards and budget at activation time.

LOG

Audit-ready by default

Every approval, tool call, and file write is logged. Export to your SIEM. Reproduce any artifact from the memory snapshot.

/ Data flow

Your data stays on your side

step 1

Your repos & specs

Codebeamer · DOORS · Git · S3

›

step 2

Local memory

Persona · project memory · code search

›

step 3

Octobus CLI

REPL · V-Model · approvals

›

step 4

Your LLM endpoint

Gemini · Claude · vLLM · Bedrock

›

step 5

Audited artifacts

commits · traceability · SIEM

Prompts go from your machine directly to the LLM endpoint you configured. Octobus never proxies your code or stores your prompts.

Stage 11 · Trust

No black box. No surprises.

Built for teams that can't afford to leak code or trust opaque vendors. Every layer is local-first, inspectable, and consent-gated.

Local-first memory

Persona, project memory, and code search index live on disk under your repo or $HOME — never uploaded by Agen-IT.

Your model, your contract

We orchestrate; we don't proxy. Your prompts go directly from your machine to the LLM endpoint you configured.

No covert telemetry

Zero analytics by default. If you opt in to anonymous usage stats, the schema is documented and inspectable in the portal.

Pinned, signed components

Lock Octobus to a build, sign skills, pin MCP versions. Nothing updates without your explicit approval.

Air-gapped option

Run fully offline with a local model (Ollama / vLLM) and local MCP servers — JLink, Saleae, filesystem, git.

Granular consent gates

Every shell command, file write, and external call passes through a hook chain you control.

lines of your code uploaded to Octobus servers

100%

of memory stored in plain markdown on your disk

Yours

model, repo, prompts, and approval gates

Stage 12 · See yourself

Where teams ship with Octobus

Embedded

Embedded Systems & Firmware

Bare-metal C/C++ on any MCU. MISRA C:2012, Unity/CppUTest, coverage — without the ASPICE overhead. FreeRTOS and Zephyr profiles included.

Aerospace

Aerospace & Avionics

DO-178C / DO-254 evidence packs, DAL A–D gate controls, PSAC traceability, HIL validation loops — profile-enforced, audit-ready.

IoT

IoT & Connected Devices

ESP32 · nRF · STM32 · Zephyr · MQTT. Profile-aware generation for constrained targets: flash budgets, power profiles, OTA signing, BLE.

Automotive

Automotive ECU

AUTOSAR / CAN / LIN drivers with ASPICE-compliant traceability from CRS to test report. Bootloader, UDS, OTA — all profiled.

Any Stack

General Software Development

Not embedded? No problem. Web, backend, CLI — the same V-Model discipline, squad helpers, and memory system, without the hardware overhead.

Embedded

Hardware bring-up & triage

Connect a J-Link, Saleae, or ESP32 and let the squad reproduce flakey signals, bisect commits, and write the regression test.

Regulated

Compliance evidence packets

Auto-generate traceability matrices, MISRA / DO-178C reports, OWASP audits, and a release dossier reviewers actually accept.

Internal Tools

Platform & DevEx

Ship internal skills, MCP servers, and PMO templates so every team inherits standards without retraining.

We tried Cursor and Claude Code first. What we kept was Octobus — because we couldn't ship the actual codebase out, and because every other tool forgot what we agreed on yesterday.

Embedded team lead

Safety-critical embedded team · early-access pilot

Quote attributable on request · pilot under NDA.

Pricing

Simple, while we're in early access

Free during early access. Pay only when you'd notice we stopped working. No card needed today.

Early access

Freewhile in beta

✓Full CLI + V-Model + Mission Squad
✓Any of 6 LLM backends · BYO auth
✓All Official skills + Tools
✓Community support
✓Local-first IP protection

Request access →

POPULAR

Pro

$29/ seat / month at GA

✓Everything in Early access
✓Private skills / plugins / profiles
✓Signed releases + version pinning
✓Email support · 1-business-day SLA
✓Audit-trail export to SIEM

Get notified at GA →

Enterprise

Customlet's talk

✓Everything in Pro
✓Air-gapped install · self-hosted assets
✓SSO (SAML / OIDC) · SCIM provisioning
✓DPA · custom retention · BAA available
✓Named architect + Slack channel

Contact sales →

Final GA pricing may change · all plans local-first by default · LLM token costs are paid to your model provider, not to Agen-IT

Stage 13 · Get started

Up and running in minutes

The CLI is one npm package. Pick any of the six LLM backends and bring your own auth — Google OAuth, Claude subscription, API key, or a local Ollama box.

▸ Node.js ≥ 20
▸ One LLM backend (Gemini CLI / Claude CLI / Antigravity / SDK key / Ollama)
▸ Python 3.10+ for the hardware sidecar (JLink, CAN, Saleae — optional)
▸ A licence JWT from portal.agen-it.com (early-access lane is free)

Request early access →

install.sh

# 1. Install the CLI
$ npm install -g @agenit/cli

# 2. Activate with the JWT you got by email
$ agenit activate ~/Downloads/licence.jwt

# 3. Pick a backend (Gemini CLI is the default)
$ npm install -g @google/gemini-cli && gemini auth login

# 4. Walk the V-Model on your project
$ cd my-project && agenit
[my-project] flow> /run requirements/CRS.pdf
   → /swe1  requirements.md         (12 SWR-NNN extracted)
   → /swe2  decisions.md            (ADRs drafted)
   → /swe3  decisions.md            (component design refined)
   → /swe4  source + @req tags      (MISRA-clean)
   → /swe5  tests/                  (unit + integration + BDD)
   → /swe6  traceability.json       (✓ 100% coverage)

Get the AI dev tool with memory

Free during early access. No credit card. Use the LLM you already pay for. We'll email your licence within one business day.

✓ Free during early access✓ No credit card✓ Cancel anytime

Any profile. Any stack.Hardware in the loop.

It remembers.

It ships.

On real hardware.

Your code stays yours.

From silicon to a tested PR — without leaving the bench

What it actually does

Remembers your code

Finishes the job

Brings your LLM

Local-first by default

Squad of helpers, not one chat

Works on what you build

vs the AI tools you've already tried

Sound familiar?

02:14 AM — pager goes off.

The pager war-room

The squad wakes — humans sleep

Your code stays on your laptop.The intelligence comes to you.

What pilot teams are seeing

Built for real engineering

Profile-Driven Co-Pilot

Standards-Native V-Model

Bring Your Own LLM

Mission Squad

Hardware-in-the-Loop

Octobus Tool Suite

Spec-Driven Development

Overnight Autonomy

From idea to a finished PR

The Octobus Tool Suite — one shell for the whole stack

Six backends. Zero lock-in.

Three layers. Clear contracts.

Octobus CLI

Your LLM

Hardware & docs bridge

Touch the REPL

92+ slash commands

Sprints, budgets, and PMO standards — built in

Plan, slice, and assign

Token + time budgets

Standards on activation

Automatic review packet

Risk before merge

Status without spreadsheets

A platform, not a binary

Bring your LLM into your world.Not the other way around.

Bring Your Own LLM

Data sovereignty

Skill governance

Audit-ready by default

Your data stays on your side

No black box. No surprises.

Local-first memory

Your model, your contract

No covert telemetry

Pinned, signed components

Air-gapped option

Granular consent gates

Where teams ship with Octobus

Embedded Systems & Firmware

Aerospace & Avionics

IoT & Connected Devices

Automotive ECU

General Software Development

Hardware bring-up & triage

Compliance evidence packets

Platform & DevEx

Simple, while we're in early access

Up and running in minutes

Get the AI dev tool with memory

Any profile. Any stack.
Hardware in the loop.

Your code stays on your laptop.
The intelligence comes to you.

Bring your LLM into your world.
Not the other way around.