Product vision
Governed autonomy, one workflow at a time.
Azimuth lets your organization decide, one workflow step at a time, how much of its software delivery the AI may do on its own, prove that the decision is enforced, and move it in either direction as the evidence and the people are ready.
The shift
The question every leader is now asked
How much is the AI allowed to do, and how do we know? Three things changed at once.
Models are getting better, fast
Context windows are expanding. Reasoning is deepening. Tool use is becoming reliable. Tasks that need human guidance today run unattended tomorrow. The question is not whether, but where, and with what controls.
Humans are moving up the stack
Writing code becomes reviewing it. Searching a codebase becomes asking an agent. Manual testing becomes defining what to test. The human role is shifting from execution to direction. Gartner's May 2026 survey found that the organizations improving ROI from autonomous systems invest in the skills, roles, and operating models that let people guide them.
Governance now ships in the tools
The major AI tools now enforce controls an administrator sets and a developer cannot weaken: blocked commands, actions that always ask, folders that cannot be written, sandboxes. They are scoped by who: an organization, a team, a user, an agent. None of them is scoped by the work. Azimuth uses the tools' controls and adds what they cannot know: your workflows.
The dial
Three settings, set per workflow step
Azimuth describes delivery as workflows: prescriptive chains of agents through one function of the lifecycle, such as a production incident or a new feature. Each step of a workflow has its own setting. A team can let the first two steps of an incident workflow run unattended while the last three wait for a person, and leave every step of the feature workflow at the first setting. The dial moves both ways.
Augmentation
“The AI helps; a person approves every change that leaves the machine”
- Team members direct agents to find code, cross-reference requirements, and generate tests
- The biggest gains land on context-heavy work: cross-repo investigation, requirements traceability, root-cause analysis
- Institutional memory stops the same investigation being repeated across sprints
- Every publish and every destructive action waits for a person
The intent of any non-trivial step, and every change that leaves the developer’s machine. The person can also approve each action through their AI tool.
Reading, searching, testing, and local work inside an approved step.
Faster teams without losing control
Delegation
“A bounded step runs on its own; a person approves it once and its result once”
- Agents complete a step end to end inside enforced limits: a root-cause document, a test run, a draft ticket
- Approval moves up: you sign off on the step and its output, not every command
- Verification is required before the next step may start
- A limit the agent crosses falls back to asking, and is recorded
A bounded step, once, and its consequential result, once.
Everything inside the step’s limits: a named branch, named files, a time limit, and a spending limit the system checks.
More throughput on the routine work, same head count
Orchestration
“Steps that fit the policy and pass verification proceed; exceptions go to a person”
- A standing policy names the step, the repositories, the limits, and the checks that must pass
- A sample of unattended runs is reviewed by a person; the sample rate is yours to set
- Anything outside the policy, or short of verification, stops and asks
- Cannot be chosen for a step until a runner exists for it
The policy for the step, and each exception the policy routes to a person.
Runs that fit the policy and pass verification, under a runner that works without a person’s session.
Chains of steps run on their own, inside the rules you wrote
What decides how far a step may go
Not how capable the model is. Two properties of the step's actions.
- Can it be undone? A local edit can be discarded. A merged change can be reverted at a cost. A dropped database cannot be recovered.
- Can it be checked? A step with tests and a quality gate can be verified without a person reading every line. A step without them cannot leave the first setting.
One workflow, five steps, one team's choice
A production incident workflow, with the settings one team might choose once the record for each step supports it.
| Step | Agent | Setting |
|---|---|---|
| Analyze production logs | Operations agent | Orchestration |
| Find the root cause | Bug-fixing agent | Orchestration |
| Open a ticket | Business analyst agent | Delegation |
| Implement the fix | Implementation agent | Augmentation |
| Open a pull request | Git agent | Augmentation |
Today every step of every workflow runs at Augmentation. The first two steps here read and write only documents, so they are reversible and checkable. The last two change code and publish it, so a person stays on them.
The controls
What never moves, and what makes the dial trustworthy
A permanent floor sits outside the dial. Destructive or irreversible actions, such as production deployments, database migrations, secret rotation, rewriting shared history, or weakening a security control or the dial itself, stop for step-by-step human approval at every setting, in every workflow. Where your AI tools can hold that floor themselves, Azimuth writes it into them. Where they cannot, Azimuth says so.
Only a person turns it up
The AI cannot raise its own setting, approve its own work for release, or edit the policy and the controls that govern it. Turning a step down is immediate and needs no justification.
Bounds are enforced, not promised
A delegated step carries limits the system checks. Where a limit can only be stated in instructions, the coverage report says so in plain words.
Readiness is evidence
Before a step can be raised, your own record must show the current setting is working: gates enforced, approvals attributed to people, verification in place, spending limited, and a named person ready for exceptions. Readiness permits a choice; it never makes it.
It steps down on its own
An attempt to bypass a control, a breach of a spending limit, or repeated failed checks returns that workflow to the first setting until a person clears it. Other workflows are untouched.
Declared and observed are both reported
Leadership sees, per workflow and step, the setting that was chosen and what the record shows actually happened.
What is true today
Enforced now: approval before every publish, the floor under destructive actions, a time-boxed and recorded relaxation of publish approval, an approval record attributed to the person who gave it, and the audit ledger.
Designed and being built, in this order: the single setting per workflow step, protection of the controls written into your AI tools, bounded step grants, readiness checks, automatic step-down, and the coverage report. Every engagement runs every step at Augmentation today.
The architecture
Every layer becomes more valuable
Each layer of Azimuth earns more as a step advances. Context prevents hallucination. Governance prevents chaos. Verification decides what may proceed.
| Layer | Augmentation | Delegation | Orchestration |
|---|---|---|---|
Context layer Requirements, tickets, code, logs, decisions | Gives agents project awareness | The knowledge a bounded step works from | Institutional memory that outlives any one tool |
Capability layer Specialized agents and skills, chained into workflows | Human-directed task execution | Bounded steps run unattended inside enforced limits | Chains of steps run under a standing policy |
Governance layer Policy, approval gates, audit trail | A person approves intent and every publish | A person approves a bounded step once and its result once | A person sets the policy and handles exceptions |
Verification Tests, quality gates, agent baselines | Checks agent quality against baselines | Gates each step before the next may start | Decides which unattended runs proceed and which stop |
Your investment
Why it compounds
Three things accumulate from the first day, and none of them can be bought later.
Evidence
The record that qualifies a step for the next setting is produced by working at the current one. Every approved publish, verified fix, and attributed decision is a line readiness reads. An organization that starts now accumulates it; one that starts later cannot buy it.
Memory
What your organization learns is kept as versioned files you own: root-cause histories, implementation notes, decisions and why they were made, dead ends already tried. Tool memories are tied to one tool and one machine and expire. Yours outlive the tool.
People
Each step that advances changes what the people on it do. The engineer who approved every root-cause analysis becomes the one who handles its exceptions. That change is planned, trained for, and measured, not discovered after the fact.
Your people
You set the pace. Your people lead the move.
Organizations set their own pace. Leaders may move the dial; Azimuth shows them what the record supports, what the move asks of their people, and how to take it back. Nothing in the product moves a setting on its own except downward.
Azimuth is a bet on elevating developers, not replacing them. As steps advance, the people on them move into the roles governed delivery needs. Each is higher-leverage than the work it replaces, and each is named in the readiness check for the step that creates it. This aligns with Gartner's May 2026 survey: workforce cuts were about as common among organizations reporting higher ROI as among those reporting modest or negative results. “Workforce reductions may create budget room, but they do not create return,” in the words of Distinguished VP Analyst Helen Poitevin.
System architects
Define how components interact and how systems evolve. This is the high-level design AI needs humans to set.
Quality governors
Set the standards AI must meet. Define what "good" means for your organization, your customers, your regulators.
Risk managers
Decide what gets automated and what needs human judgment. Set the boundaries AI operates within.
Domain experts
Translate business need into technical direction. The deep organizational knowledge that AI cannot learn from code alone.
Exception handlers
Step in when AI encounters genuine ambiguity, ethical questions, or novel situations that require human reasoning.
The moat
Why this is hard to replicate
Azimuth does not compete with the governance your AI tools now ship. It compiles your policy into it, and holds the layer above it that the tools cannot see.
Policy scoped by the work
AI tools enforce rules by organization, team, user, or agent. Azimuth sets them by workflow and step, which the tools cannot do because they do not know your workflows.
One policy, every harness
Each AI tool holds its rules in its own format. Azimuth compiles your policy into every harness you run and reports, rule by rule, where it is enforced and where it is only written down.
Institutional memory
Requires years of accumulated project context: requirements, decisions, architectural history. Cannot be bootstrapped overnight, and it belongs to you, not to a tool.
Governance framework
Requires organizational adoption, not just technical features. Delivered through consulting, not downloadable.
Self-testing harness
The framework ships a test harness for its own agents, so a provider’s model update can be checked against a baseline before it reaches your team.
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Designed for established enterprise codebases with mixed tech stacks, not just new-project demos.
Built for Claude Code. Supports GitHub Copilot. Extendable to other AI tools as required.
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Every step starts at the first setting. The record that lets you raise one is built by working there, and it starts on the day you begin.