Most AI platforms wire together agents. Durable writes software.

sync_accounts.py
1 async def sync_accounts(config):
2 # Fetch modified records from Salesforce
3 records = await sf.query(
4 "SELECT Id, Name FROM Account")
5
6 mapped = await agent.analyze(
7 records,
8 schema=AccountSchema)
9
10 await warehouse.upsert("accounts", mapped)
Agent Tools
read_file
write_file
run_tests
Solution Architect
I found 3 Salesforce custom objects relevant to your workflow, including a Customer Health Score object with 12 fields. I also detected a REST API on your internal tool.
Data Preview
Object Fields Records
Account 24 3,847
Contact 18 12,531
Customer Health 12 3,847
Draft Requirements Generating...
Sync Accounts, Contacts, and Customer Health nightly
Map custom fields to Snowflake schema...
Requirements
R1
Sync Account records from Salesforce to Snowflake every 24 hours Active
R2
Map all standard Contact fields to the contacts table Map standard + custom Contact fields, including Lead Source and Region
Modified
R3
Send Slack alert on sync failure with error details Active
Code Updated
contacts_sync.ts Updated just now
Requirements
Code
sync_accounts.py
1 async def sync_accounts(config):
2 # Fetch modified records from Salesforce
3 records = await sf.query(
4 "SELECT Id, Name FROM Account")
5
6 mapped = await agent.analyze(
7 records,
8 schema=AccountSchema)
9
10 await warehouse.upsert("accounts", mapped)
Agent Tools
read_file
write_file
run_tests

Agents as Tools, Not Architecture

Code is deterministic. It does the same thing every time, it’s cheap to run, and there are no surprises. Agents are powerful but expensive, slow, and unpredictable. Most platforms make agents the architecture. Durable makes them a tool call inside otherwise normal code, used only where you genuinely need intelligence, like classifying a document or interpreting an edge case.

  • Production code, not visual workflows
  • Deterministic by default, intelligent where it matters
  • Version-controlled, testable, and debuggable
  • Runs at the cost of code, not the cost of inference
Solution Architect
I found 3 Salesforce custom objects relevant to your workflow, including a Customer Health Score object with 12 fields. I also detected a REST API on your internal tool.
Data Preview
Object Fields Records
Account 24 3,847
Contact 18 12,531
Customer Health 12 3,847
Draft Requirements Generating...
Sync Accounts, Contacts, and Customer Health nightly
Map custom fields to Snowflake schema...

The Solution Architect

Most automation tools start with a blank canvas and assume you know exactly what to build. Durable starts by doing the work your team usually does before a project even begins: investigating your systems, asking the right questions, and turning a vague problem into a concrete spec.

Describe what’s wrong in a sentence. Durable connects to your live systems, discovers what’s actually there (schemas, custom objects, API quirks), and collaborates with your team to surface edge cases before they become production incidents. The output is a set of structured requirements you review and approve — not a workflow you hope works.

  • Connects to your APIs, databases, and SaaS tools to understand what exists
  • Asks clarifying questions instead of making assumptions
  • Loops in your team and stakeholders where decisions are needed
  • Delivers reviewable requirements, not a black box
Requirements
R1
Sync Account records from Salesforce to Snowflake every 24 hours Active
R2
Map all standard Contact fields to the contacts table Map standard + custom Contact fields, including Lead Source and Region
Modified
R3
Send Slack alert on sync failure with error details Active
Code Updated
contacts_sync.ts Updated just now
Requirements
Code

Requirements as Source of Truth

With traditional automation, the moment it’s deployed you lose the ability to understand or change it without a developer. The logic is buried in code, scattered across workflow nodes, or locked behind a vendor’s UI. When something needs to change, you file a ticket and wait.

Durable keeps your automation readable. Every automation is defined as plain-English requirements that anyone on your team can read, understand, and edit. Change a requirement and the code regenerates. No tickets, no sprints, no waiting for engineering to context-switch back into your workflow.

  • Anyone can read what the automation does, no translation needed
  • Edit in plain English, code updates automatically
  • Full version history so you can see what changed, when, and why
  • Requirements and implementation stay in sync permanently, no drift

Stop building. Start describing.