AI Agent Builder: Describe the Agent, Ship It on Your Own Records
For operations teams and IT leads who want agents that do real work, without writing code or wiring prompts to spreadsheets.
An AI agent builder usually stops at a clever prompt. That is the easy half. The hard half is binding the agent to real records, testing it on your data, controlling what it may change and keeping a version you can roll back to.
Apragya's AI agent builder does both halves. You describe the agent in plain words, it is generated as a real graph, and it goes live only once it is bound, tested and governed.
A prompt tool, against an agent builder
Apragya agent builder
- A generated graph that runs on its own
- Output saved on the record
- Bound to invoices, tickets or employees
- Published versions you can restore
- Permissions, write gates and review
An example: an overdue-invoice agent in an afternoon
Take an illustrative services firm in Bengaluru that wants overdue customers chased politely, with a person approving anything above INR 50,000.
From a sentence to an agent running on your records
Describe it
What it watches and what it should do.It is built
Generated as a real graph, not a saved prompt.Bound to invoices
Runs daily on overdue ones.Tested
On last month's invoices first.Published
A version you can roll back to.Governed
Review above INR 50,000.
The first week, the agent drafted reminders for thirty-one invoices. Twenty-six went out automatically, five waited for the finance head, and every message sat on the invoice it was about.
How chat-to-build works
Step 1 of 5
Chat
Describe the agent, and refine it in conversation.
- Chat: Describe the agent, and refine it in conversation.
- Generate: The builder produces a runnable agent, not a template.
- Bind: Choose the record type and the trigger: event, schedule or manual.
- Test: Run it against your own records before anyone depends on it.
- Publish: Release a version, with human review where you need it.
Why an enterprise AI agent builder must sit next to the records
An agent that cannot see the invoice cannot chase it. Because Apragya's builder lives inside the same platform as finance, sales, HR and support, agents work on real records from the first run, with the same permissions people have.
Standalone agent tools, against Apragya
| Feature | Standalone agent tools | Apragya |
|---|---|---|
| Data | Connected by exports or APIs | The records are already there |
| Triggers | Manual, or custom glue | Record events and schedules |
| Testing | On sample data | On your own records |
| Versions | Rarely | Published versions with rollback |
| Governance | Added separately | Access, review and audit built in |
Checked against public information on 16 September 2026.
What sets Apragya apart as a no-code AI platform
Real agents
Generated graphs that run in isolated processes.
A visual canvas
Chain agents, conditions and review steps.
Human review
Approve, reject, modify or escalate on any risky step.
Records to work on
Fifteen enterprise apps with shared data.
Share and reuse
Publish agents to the marketplace for other teams.
Visible cost
Every run metered in AI Credits.
Build your first agent
- Pick a task that follows clear rules.
- Describe it to the builder in plain words.
- Bind it to the records where that work happens.
- Test it on last month's data.
- Publish with review switched on.
Most teams publish their first useful agent in a day.
What it costs to start
AI agent builder, answered
Do we need to know how to code?
What can an agent be triggered by?
Can we undo a bad release?
Which AI models does it use?
What does it cost to start?
Describe one agent today
Start with a rules-based task and have it running on your records by tomorrow.