Kriyon's agents build, deploy, and test against your live Salesforce org — then verify every step twice, against the screen and against the data behind it. When the two disagree, the agent tells you. It doesn't call it green.
Runs against your own org · Your data never leaves your instance
The screen and the database disagree. The badge rendered from stale client state; the escalation never committed. Reporting this as a pass would have shipped a broken flow.
Most AI tools stop at output. They write the Apex, produce the test case, describe the flow — and then hand you something that looks finished and might not be. The expensive work is everything after: deploying it, exercising it in a real org, and establishing whether it genuinely did what it claimed.
That verification gap is where delivery time actually goes. Not in writing the code — in finding out, days later, that it didn't work.
An agent that can't tell when it failed isn't saving you time. It's deferring the cost.
The design principle Kriyon is built onA single observation can lie. A rendered badge can be stale, a toast can fire before the commit lands, a spinner can look like success. So no step passes on one reading — each is corroborated across independent sources before it counts.
A real browser drives the org exactly as a user would — clicking, typing, waiting for the page to settle. What the agent sees is what a person would see.
A direct query against the org confirms the record actually changed. The database is indifferent to what the UI rendered, which is exactly what makes it useful.
The agent checks what kind of control it acted on and whether that control was genuinely in the state it appeared to be — catching the acted-on-the-wrong-layer class of error entirely.
When the sources agree, the step passes and the run continues. When they disagree, the agent stops and reports the disagreement — with the evidence from each source, so you can see precisely where reality diverged from the claim. It never resolves a conflict by picking the more convenient answer.
Each one owns a stage and hands off to the next. Three of them work autonomously against a live org — those are marked. Behind them sit thirty specialist subagents handling the narrow work: locating elements, filling forms, reading flow metadata, diagnosing failures.
Turns requirements into a complete high-level design — data model, integrations, security model, non-functional requirements, and the architecture decisions with their rationale.
Breaks stories down to field-level specifications, Apex signatures, LWC component trees, and Flow definitions — detailed enough for a developer to build from directly.
Builds, deploys, tests, debugs, and retests in a single loop — proving the work on the live UI and in the data before handing it back. When something breaks, it diagnoses and fixes rather than reporting a failure and stopping.
Drives a full journey against a live org, revalidating every step against both the UI and the data — so a single bad reading never fails a test case that actually passed, and never passes one that actually failed.
Generates comprehensive test-case suites from user stories and acceptance criteria, covering the edge cases that get missed when suites are written by hand under deadline.
Takes a requirements document or use case and produces a created, validated, published, activated, and tested Agentforce agent — built entirely through Agentforce DX rather than clicking through the in-org UI.
Agents that can deploy to a live Salesforce org need an infrastructure story that holds up to a security review. Here's ours.
Each agent turn executes inside a fresh container with no Linux capabilities and a hard memory ceiling, destroyed the moment the turn ends. Nothing persists between turns except your own workspace.
Workspaces, session history, and org credentials are isolated per user and resolved server-side from an authenticated session — never from anything the browser sends.
Each session gets its own browser in its own network namespace. The remote-control port is never published to the host, so no session can reach another's browser.
Deployments and destructive org commands are gated before execution rather than left to the agent's judgment. Read-only agents are enforced read-only at the boundary.
Org credentials are encrypted at rest and resolved only server-side. The interface receives redaction placeholders — there is nothing sensitive in the page to leak.
Agents work against the org you connect. Nothing is copied to a vendor environment for processing, and no customer data is retained outside your instance.
Point Kriyon at an org and give it a story. You'll get back work that has already been deployed, exercised, and checked — with the evidence attached.