Why not just buy an off-the-shelf AI tool?

A product encodes one opinion about how the work should be done. Where your process differs, the gap becomes permanent manual effort. We start from your process instead.

How long until something is in production?

Two to three weeks to a documented architecture, then four to six to a first increment running in shadow mode against real data.

Where does our data go?

Wherever you decide — your cloud account or ours, with region and retention as explicit decisions. We do not train models on your data, and every system ships with an audit log.

Yes. Source lands in your repository from the first commit, with the architecture docs, evaluation suite and runbooks. Taking it in-house later requires nothing from us.

Discovery and architecture are a fixed fee. Build work is quoted as fixed-scope phases, so you approve cost per phase rather than signing an open-ended commitment.

How is this different from a workflow tool like Zapier or n8n?

Those execute a fixed path you drew in advance, which is exactly right when every case looks the same. An agent handles the cases that do not: it reads an unusual email, decides which of six paths applies, and stops when none of them do. We use ordinary automation for the deterministic parts precisely because it is cheaper and more predictable — the agent is reserved for the judgment.

What stops it doing something expensive or irreversible?

Three things. Tool access is granted per action, so it cannot call what it was never given. Value thresholds you define force a pause for approval. And irreversible operations are deliberately implemented as deterministic code with their own confirmation step rather than left to the model's discretion.

Can we see what it did and why?

Yes — every run stores a full trace: the trigger, the plan, each tool call with its inputs and outputs, each decision, the token and API cost, and the final outcome. Traces are replayable, which is how we debug and how you audit.

What happens when an agent gets it wrong?

It is designed on the assumption that it will. Validation failures stop the run rather than propagate. Anything uncertain routes to a person. And the failure becomes an evaluation case, so the same mistake is caught automatically on every subsequent release.

Do we need to change our existing systems?

No. Agents work through the APIs your systems already expose, and for legacy systems without one we build an integration layer around them. Nothing is replaced, and nothing depends on a migration finishing first.

How do you price this?

Discovery and architecture at a fixed fee, then build quoted as fixed-scope phases against that architecture. Running costs are model and API usage, which the trace makes visible per run — so cost per outcome is a number you can actually see.

What we buildAI Agents

Agents that finish the work, with a checkpoint wherever you want one

Most business processes are ninety per cent rules and ten per cent judgment. An agent runs the ninety, pauses at the ten, and leaves a trace of every decision it made along the way.

Map a process with us How it works

Agent run — trace view

public/agents/trace.mp4

How they are built

Traced: 100%; Every tool call, input and decision recorded and replayable after the fact.
Tool access: Scoped; An agent can reach only the systems and actions you explicitly grant it.
Always available: Halt; Any run can be paused or stopped mid-flight, by a rule or by a person.
Silent failures: 0; Anything uncertain stops and surfaces. Nothing is quietly guessed at.

01How an agent is bounded

Autonomy is a dial, not a switch

The question is never whether an agent is capable of an action — it is whether it is permitted to take that action unsupervised. Both halves are designed before anything is built.

Agent Layer

Scoped tools · Traced runs

What starts a run

Agents do not sit and wait to be prompted. They are woken by the events your business already produces.

A record changes: A deal moves stage, a ticket is raised, an invoice arrives, a form is submitted.
A schedule: Nightly reconciliation, weekly reporting, month-end close — run and checked before you arrive.
A message: An email into a shared inbox, a Slack mention, a webhook from a third party.
A person: Someone asks for it directly, from the tool they already have open.

What keeps it in bounds

Every agent ships with its limits written down and enforced in code, not implied in a prompt.

Least-privilege tools: Read-only where reading is enough. Write access granted per action, never wholesale.
Value and risk thresholds: Above your limit — refunds, credits, contract changes — it stops and asks a person.
Deterministic paths: Steps that must never improvise are plain code. The model decides only what genuinely needs judgment.
Loop and cost ceilings: Runs are bounded in steps, time and spend. A confused agent stops rather than spirals.

02Anatomy of a run

Plan, act, verify — and prove it afterwards

01
Triggered
An event in your systems starts the run, carrying the context it needs to begin.
02
Planned
The agent decomposes the goal into concrete steps against the tools it is actually allowed to use.
03
Acted
It executes step by step — reading records, calling APIs, drafting output, writing results.
04
Verified
Each result is checked against the expected shape before the next step depends on it.
05
Checkpointed
Anything above your risk threshold pauses here and waits for a person to approve or reject.
06
Closed and traced
The outcome is written back and the full trace stored — inputs, tool calls, decisions, cost.

03The hard parts

Why most agent pilots never reach production

Agents demo beautifully and fail quietly. These are the six problems that decide whether one survives contact with real data.

01
Scope creep mid-run
An agent given broad tools will find creative uses for them. Access is granted per action, so it cannot wander into systems nobody intended.
02
Confident wrong answers
Outputs are validated against a schema and cross-checked before anything downstream consumes them. Failing a check stops the run rather than passing bad data on.
03
Tool failures
Third-party APIs time out and rate-limit. Steps are idempotent with typed retries, so a retry never double-charges or duplicates a record.
04
Runaway loops
Step, time and cost ceilings are enforced by the runtime. An agent that cannot make progress halts and reports instead of burning budget.
05
No way to debug it
Every run produces a replayable trace. When something goes wrong you read what happened rather than guessing at a prompt.
06
Drift after launch
An evaluation suite built from your real cases runs against every change, so a prompt tweak cannot quietly regress last month's accuracy.

04Where they earn their place

Processes that repeat, span systems, and stall on handoffs

The best first candidate is high-volume, well-understood, and currently done by someone copying between two screens.

Order exception handling
Mismatches investigated across ERP and carrier data, resolved or escalated with the evidence attached.
Invoice processing
Extracted, matched against PO and receipt, posted — with only genuine discrepancies reaching a person.
Customer onboarding
Accounts created across every system, documents chased, and status kept current without a checklist.
Renewal preparation
Usage, tickets and contract terms assembled into a briefing before the account manager asks.
Reconciliation
Ledgers compared nightly, breaks identified and categorised, clean items closed automatically.
Internal request triage
IT, HR and finance requests classified, routed and actioned where the rules are unambiguous.

Screenshot — in context

public/ai-agents/screen-1.png

Screenshot — result

public/ai-agents/screen-2.png

05FAQ

What teams ask before they commit

Those execute a fixed path you drew in advance, which is exactly right when every case looks the same. An agent handles the cases that do not: it reads an unusual email, decides which of six paths applies, and stops when none of them do. We use ordinary automation for the deterministic parts precisely because it is cheaper and more predictable — the agent is reserved for the judgment.

06Also built here

The rest of what we do

Next Step

Let's build your next system.

Bring us the process costing your team the most hours. We'll tell you honestly which parts are worth automating.

Book a Strategy Call contact@araxys.dev

Prefer to talk now?+91 89391 53390+91 63806 69805

Fixed-scope phases
You own the source
NDA on request
No obligation

Agents that finish the work, with a checkpoint wherever you want one

How they are built

Autonomy is a dial, not a switch

What starts a run

What keeps it in bounds

Plan, act, verify — and prove it afterwards

Triggered

Planned

Acted

Verified

Checkpointed

Closed and traced

Why most agent pilots never reach production

Scope creep mid-run

Confident wrong answers

Tool failures

Runaway loops

No way to debug it

Drift after launch

Processes that repeat, span systems, and stall on handoffs

Order exception handling

Invoice processing

Customer onboarding

Renewal preparation

Reconciliation

Internal request triage

What teams ask before they commit

The rest of what we do

Voice Agents

Knowledge & Retrieval

Process Automation

Integrations

Custom Software

Let's build your next system.