AI agents that get work done,
within limits you set.

AI agent development services for real work. An agent is software that works toward a goal you set, inside the systems you allow. It stops for a person's approval before anything that can't be undone. Every step is logged.

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Reply within four business hours (typical) · Clear limits on what it can do

Brands our teams have delivered for

What we do

PixelCrayons is an AI agent development company. We build agents that carry out multi-step tasks in your systems and stop for a person before anything irreversible. You decide which tools the agent may use, and each step is logged. Scoping starts at $1,000 and is credited against the pilot, which starts at $3,000.

The right task, chosen on evidenceFirst we check the task really needs an agent, and say so if a chatbot or workflow fits better.
A goal and the tools to reach itA defined goal and set of tools, with every call the agent makes recorded.
Limited access and approval before key actionsEach tool call gets only the access it needs, and irreversible actions wait for a person.

Related work · Healthcare · US

Multi-location healthcare: delivery unblocked.

Read the case study

Our work

See what we've built
and the results it brought.

Real projects for real clients, with results we can show.

All case studies →

2004

Established

500+

Agencies served

4,500+

Projects delivered

340%

Revenue growth · 7 months

eCommerce

+127%

Organic traffic · 5 months

SaaS

85%

Faster delivery · backlog cleared

via agency partner

Where the work happens

Reply within four business hours (typical)
Scoped permissions on every tool call
Human approval before anything irreversible
Every step and decision logged
Evaluated before it runs unattended
Clear IP & Ownership Terms
Reply within four business hours (typical)
Scoped permissions on every tool call
Human approval before anything irreversible
Every step and decision logged
Evaluated before it runs unattended
Clear IP & Ownership Terms

In every build

What an agent build
actually includes.

The right task, chosen on evidence

Discovery starts by asking whether the task really needs an agent. That means several steps, several tools or systems, and a path that changes with what it finds. Much of what gets sold as "an agent" is really one answer from your documents or one fixed sequence. A chatbot or a workflow suits those better: faster to build, with less to control. If your task is one of those, the discovery document says so.

Week zero

A goal and the tools to reach it

The agent gets a goal and a set of tools, such as a CRM lookup, a search, an API or a database read. It plans its own steps toward that goal. It remembers what it has learned along the way instead of starting from scratch each time. Every call it makes, and why, is recorded. That planning is the real engineering. A demo that just chains three prompts together isn't the same thing.

Architecture

Limited access and approval before key actions

This is not a black box that acts on its own. Each tool call gets only the access that step needs, never a master key to your systems. Some actions can't be undone, are seen outside your business or go above a limit you set. Sending, spending and changing a live system are examples. These wait for a person before they happen. The agent suggests the action and explains why, and a person decides. Important decisions stay yours.

Every build

Evaluated before it runs unattended

Before an agent works alone on a real task, it runs under supervision on cases from the real job. These include cases where the obvious next step is the wrong one. We measure its accuracy against your own starting point, not a vendor's test. The tests run again whenever its tools or data change. A limit set for last quarter's system may not hold for this quarter's.

Before launch

Handover, logs and ownership

Every run is recorded: the request, each call, each decision and what a person approved or changed. At handover you get the runbook (a written guide) and admin training, under IP and ownership terms agreed before work starts. They show how to change its access, add a tool, read the logs or switch the agent off. After launch, the usual choice is a light retainer for monitoring and the changes the first month shows up. Running it fully in-house is fine too.

At handover

The pilot

From a scoped goal
to a working agent.

Start

Brief and a call

We sign an NDA and map the task with you. We check it needs an agent, not a workflow or a chatbot.

Once the brief is complete

Itemised proposal

We confirm the date your proposal arrives. It sets out the goal, the tools the agent can use, its access limits, the approval rules and the price.

Your decision

You sign off

You confirm the scope, the payment and the access we need. Nothing starts before this.

Typically 2 to 3 business days later

The work starts

We connect the agreed tools and approval steps. Then we build the pilot agent for one real task from your business.

On the date in your proposal

First deliverable

A working agent runs that task, supervised and logged. It runs alone only where its measured accuracy supports it. Your team learns the runbook.

Inside Prism

Your engagement, week to week,
in one workspace.

Prism is the client workspace where your requests, approvals, tasks and weekly reviews live.

Some engagements include Prism, our client workspace, and your proposal says whether yours does. Where it does, your AI build runs in a workspace you log into: requests, approvals, the task list and the weekly review, with each decision and its expected result written down.

See how Prism runs an engagement

01
Requests and approvals
One queue, one owner, one due date.
02
Weekly review
Each decision recorded with its expected result.
03
Actions checked against outcome
What we did and what happened, side by side.

How one agent run works

An agent works on its own,
but a person still checks it.

An agent that plans its own steps still needs limits on what it may touch. This is the loop every run follows: plan, use a tool, check the limits. It shows where a person steps in.

The run

01
Goal & scope
The agent gets a clear goal and a list of what it may touch: named tools and named systems, nothing assumed.
No decision here
Nothing runs yet. A goal with unlimited access is the wrong starting point. So the goal and the access are agreed together, in writing, before the agent runs.
02
Plan
The agent breaks the goal into steps. At each point it decides what to do next: read a record, call an API or ask a question.
Unclear or missing information
The agent asks rather than assumes. A plan built on a guess gets worse with every step that follows. So an unclear step pauses for input instead of making up an answer.
03
Tool call
It calls the tool or system the step needs, such as a lookup, a search or a database read. Its login gives it only what that call needs.
Tool fails or returns nothing useful
Logged and retried by set rules, or handed to a person. The agent doesn't make up a result to keep going. That is the most common way an agent quietly produces nonsense.
04
Limits check
Before anything irreversible or public happens, the planned action is checked against its access and the approval rules agreed at the start.
Above the approval limit, or outside its access
Held for a person to sign off before it runs. Sending something, spending money or changing a live system: you set these limits, not the agent.
05
Act & log
The approved action runs, and the step and its reasons are logged. Then the agent checks whether the goal is met or another step is needed.
The next system refuses the change
Undone back to the last good state and reported. Our automation builds work the same way. A half-finished action left open is worse than a run that simply stops.

Where a human is built in

The rule behind the whole loop: an agent that stops and asks costs you a few minutes. One that goes beyond its access can cost you an audit.

Illustrative run: a generic multi-step task, not client configuration

How we report it, in Prism

The eval scorecard,
before the pilot goes near a customer.

What you receive in month one

AI work is reported against an evaluation set built before any model is chosen. In the first month you receive the task definition with the inputs and acceptable outputs written down, the eval set of real cases with expected answers, and the scorecard showing accuracy, refusal behaviour and cost per run. The go/no-go checklist names what must hold before the pilot touches live traffic.

01Task definition: inputs, acceptable outputs, and the cases the system must decline
02Eval set: real cases with expected answers, reviewed by your team
03Scorecard: accuracy, refusal behaviour, latency and cost per run
04Go/no-go checklist: the thresholds the pilot must meet before launch
05Failure log: each wrong answer, its cause, and the fix applied

Related workMulti-location healthcare: delivery unblocked.Healthcare · US · A different discipline, so it is linked here rather than presented as proof of this one.

Our other AI services

Other ways we can automate your work

If the path is fixed and runs at volume, that is workflow automation. If it's one question with one answer from your documents, that is a chatbot. Not sure which you need? Discovery tells you in writing, before anything is priced.

AI Voice Agents AI Receptionist Workflow Automation AI Chatbots Hire AI Agent Developers All AI solutions

Who is accountable

The people responsible
for your work.

Leadership and how we’re built →

Parvessh Agarwal

Founder & CEO

An entrepreneur at heart. He links business analysis, strategy, clear goals and ROI to practical technology.

LinkedIn →

Saundaraya Gupta

Technical Manager

Brings technical work, client updates and project management together, so work moves smoothly from brief to launch.

LinkedIn →

Every engagement

A named coordinator and escalation contact

Know who coordinates your engagement, who owns the work and how to escalate an issue.

All engagements. Roles may be combined and vary with your engagement.

Full terms →

Before you sign

Our commitments

Each one says where it applies and links to its full terms.

Response and start times

We typically respond within four business hours. Eligible engagements can typically start within two to three business days after scope, payment and required access are confirmed.

Business hours are Monday to Friday, 9:00 am to 6:00 pm IST, excluding published company holidays. Typical expectations, not guaranteed service levels.

Full terms

Clear IP & Ownership Terms

Your materials remain yours. Bespoke deliverable rights, licences and handover are agreed before work starts.

All engagements.

Full terms

Clear Delivery & Escalation Ownership

Know who coordinates your engagement, who owns the work and how to escalate an issue.

All engagements. Roles may be combined and vary with your engagement.

Full terms

How we handle claims, credentials and your data: group credentials, dated ratings, how case figures are signed off, client permissions and data handling.

Before anything is built

Agent, workflow or chatbot?
Which build your task needs.

Discovery starts by asking whether a task really needs an agent. Much of what gets sold as an agent is one answer from your documents or one fixed sequence. Those are faster to build, with less to control. This is the table we decide from.

Your task	What we would build	Why
One question that needs a grounded answer from your documentation	A chatbot	It answers one question at a time and hands over to a person when it is unsure. There are no planned steps to control.
One path you have already mapped, run at volume: intake, sorting, data entry	Workflow automation	A fixed path runs reliably, with a person checking the judgement step.
Several steps and several tools, on a path that varies with what it finds	An agent, starting with a scoped pilot on one real task	This is the work an agent is for. It plans its own steps toward a goal, and that planning is the engineering.
Any step that sends, spends or writes to a live system	An approval step on that type of action, at a limit you set	The steps are not fixed in advance, so the check sits on the action wherever it comes in the run.
A step that never proves safe enough in supervised runs	That step stays supervised	A step runs alone only where its measured accuracy supports it. We say so plainly when it does not.

Not sure which you have? The discovery document says which, in writing, before anything is priced.

Questions

Frequently
asked.

A chatbot answers one question at a time from your documents and passes to a person when it's unsure. An agent gets a goal and works toward it over several steps. At each step it decides what to look up or call next. That's why an agent needs tighter limits on access and more approval steps.

Workflow automation runs one path you've already mapped, at volume, with a person checking the judgement step. An agent has no fixed path. Given a goal and tools, it decides each step as it goes. So the check sits on the type of action: anything irreversible or above a limit, wherever it comes.

Any model can make a wrong call, so we limit what it can do. Each tool call gets only the access that step needs. Anything irreversible or above your limit waits for a person's approval. Before it runs alone, the agent is scored against your own starting point. Every step is logged, so a wrong call is easy to see.

Your existing tools. The agent uses the CRM, ERP, helpdesk, internal APIs and spreadsheets you already run. You don't move to a new platform first. We don't favour any one model. We choose it in discovery on fit, cost and where your data must be stored. The connections let you swap the model without rebuilding the agent.

Under supervision, on real cases from the task, including cases where the obvious next step is wrong. We check that it calls the right tool, knows when it lacks information and waits for approval when it should. You see the scores against your own starting point before sign-off. The tests run again when the tools or data change.

A scoping sprint starts at $1,000: one to two weeks to map the task and tools and set a fixed pilot quote, credited against the pilot. A pilot on one task with up to 3 tools starts at $3,000. Running and monitoring a live agent starts at $300 a month for up to 2,000 runs. Model and API usage and hosting are billed separately, and prices exclude taxes.

The range

What it costs,
at the direct rate.

A scoping sprint first checks whether an AI agent suits the task at all. Then comes a pilot on one real task. Once the agent has proved it can run alone, it moves to a monthly fee.

One-offScoping and evaluation sprint$1,000–$6,250one-time
ProjectAgent pilot$3,000–$31,250fixed quote, one-time
RetainerProduction run and monitoring$300–$3,750per month

Indicative: every figure is refined into an itemised proposal.INR figures shown excl. GST.

Cost guideHow much does an AI agent cost in 2026?The options, the drivers and the range →Rate cardAI agents beside every other serviceDirect rate, in ₹ and $ →

Practitioner notes
on this work.

All articles

29 August 2026 · 5 min read

AI agent vs automation vs a chatbot: the questions we ask before building an agent

A chatbot answers, an automation follows a path drawn in advance, and an agent chooses its own next step.

25 August 2026 · 5 min read

Why governed delivery says no to some AI automation, and where the approval step goes

Most AI automation requests are good ideas. Some tasks should not run unsupervised however capable the model is. How governed delivery tells the two apart.

Give an agent one real task,
with the brakes on.

Scope a pilot: one task, the tools it may call, a working agent with scoped permissions, a person's approval before anything irreversible, and every step logged.

Get a Proposal Get a Free AI Automation Review

Reply within four business hours (typical) · NDA standard · Clear IP & Ownership Terms

Rankings on this topic

Best AI Agent Development Companies (October 2026) (PixelCrayons is listed, not ranked)
Best AI Development Companies (October 2026) (PixelCrayons is listed, not ranked)
Best Chatbot Development Companies (October 2026) (PixelCrayons is listed, not ranked)
Top Chatbot Development Companies in India (October 2026) (PixelCrayons is listed, not ranked)

Scored on public evidence by the PixelCrayons Index. PixelCrayons is scored like every firm: ranked where it has a case study, listed where it has not.

Last updated 4 November 2026

AI agents that get work done,within limits you set.

See what we've builtand the results it brought.

What an agent buildactually includes.

The right task, chosen on evidence

A goal and the tools to reach it

Limited access and approval before key actions

Evaluated before it runs unattended

Handover, logs and ownership

From a scoped goalto a working agent.

Brief and a call

Itemised proposal

You sign off

The work starts

First deliverable

Your engagement, week to week,in one workspace.

Requests and approvals

Weekly review

Actions checked against outcome

An agent works on its own,but a person still checks it.

Goal & scope

Plan

Tool call

Limits check

Act & log

The eval scorecard,before the pilot goes near a customer.

Other ways we can automate your work

The people responsiblefor your work.

Parvessh Agarwal

Saundaraya Gupta

A named coordinator and escalation contact

Our commitments

Response and start times

Clear IP & Ownership Terms

Clear Delivery & Escalation Ownership

How do we know the work will be good?

Who owns the ad accounts, the data and the work?

What if we want to stop?

How will we know what you are doing?

Agent, workflow or chatbot?Which build your task needs.

Frequentlyasked.

What it costs,at the direct rate.

Practitioner noteson this work.

AI agent vs automation vs a chatbot: the questions we ask before building an agent

Why governed delivery says no to some AI automation, and where the approval step goes

Give an agent one real task,with the brakes on.

AI agents that get work done,
within limits you set.

See what we've built
and the results it brought.

What an agent build
actually includes.

From a scoped goal
to a working agent.

Your engagement, week to week,
in one workspace.

An agent works on its own,
but a person still checks it.

The eval scorecard,
before the pilot goes near a customer.

The people responsible
for your work.

Agent, workflow or chatbot?
Which build your task needs.

Frequently
asked.

What it costs,
at the direct rate.

Practitioner notes
on this work.

Give an agent one real task,
with the brakes on.