Catalog
concept#Artificial Intelligence#Software Engineering#Integration#Platform

AI Coding Agents

Concept of autonomous AI assistants that support developers with coding tasks, orchestrate workflows, and combine tools.

AI coding agents are autonomous software assistants that support developer tasks like code generation, refactoring, and test creation.
Emerging
High

Classification

  • High
  • Technical
  • Technical
  • Intermediate

Technical context

GitHub/GitLab for repository and PR managementCI/CD systems (Jenkins, GitHub Actions, GitLab CI)Identity providers and SSO for secure authentication

Principles & goals

Human-in-the-loop: Critical decisions require human validation.Least-privilege: Agents are granted only minimal required permissions.Transparency: Actions, data sources and confidence values must be auditable.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Introduction of security vulnerabilities via unreviewed changes.
  • Excessive reliance on agents reduces team expertise.
  • License and copyright issues from trained models.
  • Always require human reviews for security-relevant changes.
  • Operate agents with minimal necessary permissions only.
  • Ensure transparent logs and reproducibility of actions.

I/O & resources

  • Access rights to code repository
  • Test suite and CI configuration
  • Requirement description or issue ticket
  • Suggestions or automated pull requests
  • Generated tests and test data
  • Action logs and audit trails

Description

AI coding agents are autonomous software assistants that support developer tasks like code generation, refactoring, and test creation. They orchestrate tools, leverage repository context, and can execute autonomous workflows. Adoption improves productivity but requires governance, security controls, and integration strategies to mitigate misinformation and dependency risks.

✔

  • Increased developer productivity by automating repetitive tasks.
  • Faster iteration through automated code and test generation.
  • Standardization of processes and consistent PR quality.

✖

  • Hallucinations: Generated code can be incorrect or insecure.
  • Dependence on external models and API availability.
  • Not all domain problems can be reliably automated.

  • PR throughput per week

    Number of pull requests initiated or prepared by agents per week.

  • Test pass rate for generated code

    Percentage of automatically generated changes that pass all CI tests.

  • False positive rate in security checks

    Share of reported security issues that prove to be non-critical.

Proof-of-concept: auto-generated feature branches

Pilot created feature branches and tests for small bugfixes; maintainers used agents as suggestion sources.

Internal CI agent integration

Company integrated agents into CI for test generation; increased coverage but required additional security reviews.

Open-source experiment with PR templates

Community used agents to create standardized PR templates and changelogs; helped with consistency and documentation.

1

Start a pilot with a clearly limited scope (e.g., test generation).

2

Train agents with read-only context access and evaluate in an isolated environment.

3

Introduce governance rules, review processes and rollback mechanisms.

4

Perform security and privacy tests, monitor API quotas.

5

Gradually expand scope with continuous metric monitoring.

⚠️ Technical debt & bottlenecks

  • Short-term integrations implemented without versioning.
  • Vendor-API specific adapters increase coupling.
  • Unclear ownership of agent-generated artifacts.
Access rights and data availabilityAPI latency and costs for external modelsQuality and relevance of training and context material
  • Allowing agents to automatically publish releases without security checks.
  • Using agents as the sole source for architectural decisions.
  • Unlimited API key exposure to third-party agents.
  • Underestimating ongoing maintenance and API costs.
  • Missing rollback strategy for faulty agent actions.
  • Insufficient training and test data for domain-specific tasks.
Prompt engineering and prompt-specific testingSoftware architecture and DevOps experienceSecurity assessment and compliance knowledge
Access to repository and context dataSecure authentication and permission managementObservability and auditability of agent actions
  • • Regulatory requirements and data protection rules
  • • Limited compute resources or API quotas
  • • Licensing terms of training data and models