Verification for coding agents

Coding-agent reliability comes from a process that makes claims testable and failures visible. Define the work, capture evidence, run relevant checks, and review the result. Stop or request input when the evidence is insufficient. Atomic provides the runtime for encoding this process as a workflow.

A seven-part verification model

1. Use workflow-shaped execution. Define the objective, constraints, done criteria, and unacceptable outcomes. Run the work through bounded stages with scoped context, tools, and explicit handoffs instead of one opaque prompt.
2. Preserve evidence and receipts. Record claims, produced diffs, executed commands, observed outputs, encountered blockers, and remaining risks. Keep the artifacts required to inspect those claims after the stage ends.
3. Enforce structured contracts. Validate schema-backed handoffs, reviewer decisions, and failure states before later stages consume them.
4. Review adversarially. Use fresh-context verifier passes to inspect the actual work and its evidence against the acceptance contract.
5. Reduce evidence into a decision. Use code-defined gates to reduce structured results into deterministic approve, retry, block, or human-escalation decisions.
6. Validate before approval. Run relevant checks through the runtime and external tools. Require human input before risky steps when the workflow calls for it.
7. Checkpoint long-running work. Preserve completed workflow operations so interrupted, long-running, or parallel work can resume without unnecessarily repeating supported side effects.

When Atomic authors this process from a natural-language request, it reads the workflow documentation and maps the objective, constraints, and success criteria to relevant implementation primitives. It can configure explicit stages, parallel branches, contracts, artifacts, checks, independent review, gates, and human-input points where the workflow requires them. The result is executable TypeScript, not a fixed black box: developers can inspect the generated definition and adjust its verification strategy, tools, models, stage boundaries, or controls as needed.

Checks should test the public behavior

Choose executable checks that match the claim and run them before approval. Use a production build to verify buildability, integration tests for user-visible behavior, static analysis for known code constraints, link and schema parsing for generated pages, or browser automation for an end-to-end flow. Workflows can invoke runtime checks and external tools, then require human approval before a risky step. An unrelated passing test is not evidence for the behavior under review.

Fresh-context reviewer stages can inspect the actual diff, artifacts, receipts, and check output against the acceptance contract. This creates an adversarial verification pass: the reviewer tests the implementation’s claims instead of continuing from the implementer’s assumptions. Independence is a workflow design choice; adding more agents does not provide it automatically. A reviewer using the same assumptions and incomplete evidence can repeat the same mistake.

Artifacts and contracts make the run inspectable

Atomic workflows can preserve research documents, implementation notes, diffs, commands, logs, transcripts, reviewer findings, check output, blockers, and final summaries. Stage receipts connect claims to persistent evidence and identify remaining work or risk. These artifacts keep the run inspectable after its model context is gone.

Schema-backed output contracts make handoffs testable. A stage can require structured results for findings, evidence, decisions, and failure states. Validation errors can trigger bounded corrective prompts. If the result still does not satisfy the schema, the stage fails instead of passing malformed evidence downstream.

Failure, retries, and stop conditions

A workflow should distinguish retryable execution failures from evidence that the implementation is wrong. Retrying a flaky network operation can be appropriate. Repeating an implementation step until a check happens to pass can hide a defect. Define attempt limits and surface the last failure. A code-defined reducer can evaluate the structured evidence and select approve, retry, block, or human escalation. That decision is deterministic for the evidence it receives; model generation and reviewer judgment are not.

Checkpointing prevents Atomic from unnecessarily rerunning completed, supported ctx.* workflow operations after an interruption, including operations in long-running or parallel workflows. It does not checkpoint ordinary TypeScript outside those operations. Checkpointing also does not replace source-control checkpoints, test evidence, or human approval; each addresses a different problem.

Build the loop, then inspect the evidence

Start with the smallest loop that has meaningful acceptance conditions. Add a stage only when it creates a clearer boundary, a better check, or a necessary decision point. Learn how to structure coding agent workflows for software engineering, compare the review controls in Atomic’s built-in workflows, then use the TypeScript SDK guide to encode contracts and gates. The resources index links to maintained documentation and source.

This guide is a practical overview of verification design. The complete technical documentation remains the current source of truth for the workflow primitives and runtime behavior that implement it.