Workflow agents
Agents that carry a multi-step task through your product, such as intake, triage, follow-up or scheduling, and stop where a person needs to decide.
AI agents · Planning Sprint · 2 weeks · $5,000
AI agent development. For products with real customers.
We build AI agents that do real work inside your product: they act through your own APIs, inside each user's permissions, with a person approving what matters, and they are tested on your own cases before they touch customer data.
A person approves
Wherever a mistake would cost money or trust
Within a week
The Planning Sprint starts after you book
Takes longer?
No extra cost if within the agreed scope
Agents live inside the products of
Agent run · Reschedule a sick technician's visits
Fieldline
- get_visitsYour API · 6 visits booked for Dana today
Agent workingApproveReview
Illustrative example on a fictional product. Not client work.
What we build
Agents that act. Not chatbots that talk.
A production AI agent plans and takes actions in your product on a user's behalf. Most agents that fail do so at the edges: what they may do, whose permissions they use, and what happens when a step goes wrong. We design those edges first. Read why AI agents fail in production and how to add agents to an existing product.
Agents that act through your APIs
The agent calls the same APIs your product already exposes, so every action passes your business rules and leaves a record.
Supervised agents with human review
The agent drafts, a person approves. Nothing reaches a customer, a record or a payment until the right person signs off.
Multi-agent workflows
Several focused agents, each with its own tools and limits, coordinated on one job, where a single agent would carry too much.
Demo agent or production agent
Four limits. Designed before the first prompt.
These decide whether an agent can be trusted with customer data, and each one goes deeper in our guides. Below them, how a production agent differs from one that only works in a demo.
A fixed list of actions
The agent can only do what we gave it a tool for. No open-ended access to your systems.
Guardrails for AI agentsPermissions where the data lives
Each action runs with the user's own permissions, checked by your product, not by the prompt.
AI agent securityPeople at the points that matter
Approval steps sit wherever a mistake would cost money, trust or compliance.
Human in the loop AITested before release, watched after
Every agent is tested on your own cases before customers see it, and monitored once it is live.
How to test an AI agentA demo agent
It works when nothing goes wrong.
- What it can doWhatever the prompt allows
- Whose access it usesOne service key with broad access
- Who decidesThe model
- How it is checkedA few good runs in a demo
- When something goes wrongHard to tell what happened
- Who answers for itYour team, once it is switched on
- After releaseYour team finds the problems
Your team still has to make it safe.
A Datics production agent
Built for the day something goes wrong.
- What it can doA fixed list of actions, one tool for each
- Whose access it usesEach user's own permissions, checked by your product
- Who decidesA person approves wherever a mistake would cost money or trust
- How it is checkedYour own cases, including tool failures and unclear inputs
- When something goes wrongEvery action logged, with a runbook to trace it
- Who answers for itWe own the delivery, to the done criteria agreed up front
- After releaseMonitoring in place and a two-week warranty
- Fixed actions
- People approve
- Every action logged
Try it
Try to break the agent.
Pick what goes wrong and watch what a production agent does about it, down to the line it writes in the log.
Pick what goes wrong
A demo agent is judged on the good runs. A production agent is judged on these. Every one of them is in the test set before release.
Agent run
Fieldline
Sam, dispatcher asks
“Dana is off sick. Move today's visits and tell the customers.”
- get_visits6 visits booked for Dana today
- find_open_slotsYour API timed out
- find_open_slotsRetried once, timed out again
What the agent does
Stops and tells Sam what failed. No visit is moved and no message is sent.
log · run 8813 · find_open_slots failed twice · stopped, dispatcher told
Illustrative example on a fictional product. Not client work.
Want to click through a real product with AI built in? Try our working demo.
How it runs
One agent at a time. Tested before it acts.
Four steps, from a free call to an agent your customers use. Every one ends with something you keep.
30 minutes
No chargeProduct Review
You show us the product and the job. We tell you plainly whether an agent fits, or whether simpler automation would do.
You keep
A straight answer on fit
Weeks 1 and 2
$5,000Planning Sprint
Starts within a week of booking. We define the agent's job, its actions, where a person approves and its test cases.
You keep
An agent brief and a fixed quote
Every two weeks
Fixed quoteBuild Sprints
Built in your code and cloud, run against real cases from your product, with a working demo every sprint.
You keep
A demo and test results every sprint
When live
2-week warrantyIn production
Released to your customers with monitoring in place. The next agent gets its own quote, only if you want it.
You keep
The agent, its tests and its runbook
Sample agent brief
End of week 2
- Job
- Move a sick technician's visits to open slots and tell each customer
- May use
- get_visits, find_open_slots, move_visit, draft_message
- Never
- Cancel a visit, change a price, message without approval
- A person approves
- Every customer message, before it is sent
- Tested on a set of your own past schedule changes
- Tool failures and unclear inputs in the test set
- One fixed quote to build it
Illustrative example on a fictional product. Not client work.
Want our engineer in your team?
Forward deployed engineers.
Our engineer joins your standups, repositories and reviews, and stays until the outcome is live. It starts with a one-week, $2,500 Outcome Sprint.
See forward deployed engineersAlready built a demo?
Start with the AI Scorecard.
It tests what you built on 50 of your own cases and ranks what to fix before it reaches every customer. $5,000, two weeks, and the fee counts toward production.
Take a demo to productionTry it
Not sure which start fits?
Four questions, and we show the start that fits your product, with what it costs.
- 01Is your product live, with customers using it?
- 02Have you already built an AI feature or agent?
- 03Do you know the outcome you need?
- 04Do you have engineers who would work alongside ours?
Four questions
Find the right way to start.
Answer all four and we show the start that fits your product, with what it costs. Your answers go with your booking, so the call starts where you are.
In practice
How it ran in live products.
Six agents acting inside products customers already use, and how the work ran.
Real estate and hospitality
CleanHaus
An agent that runs bookings, jobs, inventory and contractor outreach through CleanHaus's own APIs.
How it ran
- A tool allowlist per channel and role, under each user's own login
- Contractor bids wait for the owner's approval
- Shipped through pull requests in CleanHaus's own GitHub and AWS
Financial services
Cosmic AI
Agents that research and prepare each trade in chat, and nothing is placed until the investor confirms.
How it ran
- Changed while live, with users already on the product
- Every change through a separate QA environment before production
- Product and design calls in weekly syncs with the founder
Automotive and dealerships
Coast Technology
A sales assistant that recommends real units from live inventory and hands leads to Coast's CRM.
How it ran
- Rebuilt around tools after a dealer's own structured test
- Stabilized with a few live dealerships before scaling
- Each round of test notes written into Coast's requirements
Sales training (EdTech)
Insurance Sales Lab
An AI prospect its agents can call any time, scored on the company's own script and checklist.
How it ran
- Inside its platform, with no separate sign in
- Admins own the scripts, scenarios and scoring standard
- A demo recording after each step, its feedback in the next sprint
Financial services
A regulated investment marketplace
Agents that read pitch decks and financials and suggest the fields of a new deal for the issuer.
How it ran
- The issuer applies, edits or rejects every suggested field
- Each agent delivered as an API for the client's own .NET team
- Demoed to the client before acceptance testing
Healthcare
A dental clinical documentation company
AI drafts the clinical note during the appointment, and only the dentist can release it.
How it ran
- Shipped in the product's own stack and process
- Every change checked against one rule: no note leaves without approval
- Built across the product's existing AWS and Google Cloud setup
More agents we built
- SovrenAIAn app builder agent where every guessed join between tables waits for a person to approve it
- A regulated investment marketplaceInvestor agents that recommend deals and draft interest, while every investment decision stays with people
- MIRAAI patient intake any clinic can sign up for and run, with patient names never sent to the model
- AmoveA Slack assistant for the team, with stored answers first and sourced answers after
- OCCIIts Microsoft Copilot agent tested in its own tenant, every answer checked against SQL
- FLZRSupport chatbots that hand each shopper to a sales agent within the hours the business sets
What clients say
Agents their customers already use.
The founders and CTOs whose products run our agents, in their own words.
CleanHaus · App and AI agent live
“CleanHaus has been moving quickly to deploy our app and Broomy AI Agent with the key assistance of the Datics team. We couldn't have reached this point without their excellent bench of engineers.”
CleanHaus · Every agent run traced and logged
“I want to recognize and let you know that your work is having a truly positive outcome. Our recent tests are going so much better!”
MIRA · AI patient intake, now a commercial product
“Amazing level of proficiency and responsiveness. We are looking forward to moving on to our next project!”
Coast Technology · Sales assistant in production
“You've always achieved everything that we've asked for, and it's amazing. The quality of your work is second to none, and we couldn't be more pleased.”
5.0 average client rating
Questions
AI agent development, answered.
Adding other AI to your product as well? See AI integration services.
How much does it cost to build an AI agent?
It depends on how many actions the agent takes, the systems it touches and where people approve its work. You can start with the $5,000, two-week Planning Sprint, which defines the agent and ends with a fixed quote for building it. The sprint fee counts toward that quote.
How long does it take to build an AI agent?
The Planning Sprint takes two weeks. The agent is then built in two-week sprints and tested on your own cases before release; the number of sprints depends on the agent, and you know it before work starts.
How soon can you start on a new AI agent?
The Planning Sprint starts within a week of booking.
What if building the AI agent takes longer than planned?
Within the agreed scope, that is on us: the fixed quote does not change. If you add to the scope, we agree the change and its price with you before any work on it starts.
What happens after the AI agent is released?
Every agent comes with a two-week warranty after release: anything that does not meet the agreed done criteria is fixed at no extra cost. Further work is quoted on its own, only if you want it.
Who is accountable for delivering the AI agent?
We are. Datics owns the delivery of each agent, with our own team and process, to the done criteria agreed in the Planning Sprint and at the fixed quote. You review the work and the demos; you do not manage the people doing it.
Who builds the AI agent?
Senior Datics engineers who have shipped AI in production inside live products, backed by the wider Datics team on architecture and applied AI. If you would rather have our engineer work inside your own team, that is a forward deployed engagement.
What do your AI agent development services include?
Workflow agents, agents that act through your own APIs, supervised agents with human review and multi-agent workflows. Each one starts with a Planning Sprint that defines its job, actions, approvals and test cases, then is built in two-week sprints, tested on your own cases and released with monitoring.
What should we look for in an AI agent development company?
Agents already running inside live products, not only demos. Agents that act through your own APIs and each user's permissions, with people approving what matters. Testing on your own cases before release, a fixed quote, and the code in your repositories.
Which agent framework do you use?
The one that fits your stack and the job. We often use LangGraph, LangChain and CopilotKit, with models from AWS Bedrock, Azure AI or Google Vertex AI. What matters more than the framework is the layer around the model: its tools, permissions, approvals and tests.
Can an agent work with our existing APIs?
Yes, and we prefer it. An agent that acts through your own APIs inherits your business rules, permissions and audit trail. Where an API is missing, the Planning Sprint names it and adding it is part of the fixed quote.
How do you test an AI agent?
On real cases from your product, before customers see it: what it should do, what it must never do, and how it behaves when a tool fails or the input is unclear. Once it is live, monitoring flags drift and failures so they are fixed quickly.
What happens if the agent makes a mistake?
It is designed so that a mistake is caught before it matters. Risky actions wait for a person's approval, every action is logged, and the agent can only use the tools and permissions it was given. When something goes wrong, the log shows exactly what happened.
Have work you want an agent to take on?
Show us the product and the job. In 30 minutes you will know whether an agent fits, what it would need and how the Planning Sprint would define it.