Building a coding agent that ships safe code to production

The Challenge

Building an AI agent that writes, edits, and refactors code across a full codebase is one of the hardest problems in AI. The agent needs to reason about multi-file dependencies, respect project conventions, and - critically - never make a destructive change.

A startup building a coding agent for enterprise engineering teams faced existential reliability problems:

• Destructive file operations - The agent occasionally deleted files, overwrote configs, or ran rm -rf on directories when it misunderstood instructions
• Secret leakage - Generated code sometimes hardcoded API keys, database credentials, or tokens found in context
• Cross-language inconsistency - The agent produced solid Python but generated unsafe Go (missing error handling) and insecure TypeScript (type coercion vulnerabilities)
• Silent regressions - After model updates, previously safe behaviors would break without warning
• No evaluation framework - The team had no way to systematically measure whether the agent’s code output was improving or degrading

The Solution

Future AGI provided the evaluation and safety infrastructure the team needed to ship their coding agent with confidence.

Multi-Language Code Evaluation

Every code generation was evaluated against language-specific quality rubrics:

• Python - PEP 8 compliance, type annotations, proper exception handling, dependency security
• TypeScript - Strict type safety, null checks, no any escape hatches, secure DOM operations
• Go - Exhaustive error handling, goroutine safety, proper defer/close patterns
• Rust - Ownership correctness, minimal unsafe blocks, no unwrap-on-Option patterns

The evaluation pipeline ran on every generation before it reached the user, catching issues the agent’s own reasoning missed.

Destructive Operation Blocking

A real-time guardrail layer intercepted dangerous operations before execution:

• File system - rm -rf, recursive deletes, and overwrites of critical files (.env, package.json, Cargo.toml) blocked
• Git operations - Force pushes, history rewrites, and branch deletions held for confirmation
• Database - DROP TABLE, TRUNCATE, and schema-breaking migrations flagged
• Secrets - Any generated code containing patterns matching API keys, tokens, or credentials was rejected before output

Regression Detection

After every model update or prompt change, the team ran the full evaluation suite against a curated benchmark of 500+ code generation scenarios. Regressions were caught before reaching any user.

Step-Level Tracing

For multi-file edits, every step was traced - which files were read, what changes were planned, what was actually written. When the agent made a mistake, engineers could replay the exact reasoning chain.

The Results

• 99.2% code safety score across all supported languages
• Zero destructive operations reached production after guardrail deployment
• Secret leakage eliminated entirely - zero instances post-launch
• Cross-language consistency achieved through per-language evaluation rubrics
• Regression detection caught 12 breaking changes during model updates before they shipped
• Enterprise customers gained confidence to deploy the agent in production codebases