Optio – Orchestrate AI coding agents in K8s to go from ticket to PR

Deploys and manages multiple AI coding agents as containerized workloads in Kubernetes clusters, using K8s primitives (Pods, Services, ConfigMaps) to handle agent lifecycle, scaling, and resource allocation. Agents run in isolated containers with configurable compute limits, enabling horizontal scaling of parallel code generation tasks across cluster nodes. Integrates with K8s API for service discovery and inter-agent communication.

Automatically converts GitHub issues into pull requests by extracting issue context (title, description, labels, linked code), generating code changes via AI agents, and submitting PRs back to the repository. Integrates with GitHub API to read issues, create branches, commit changes, and open PRs with automated commit messages and PR descriptions. Handles branch naming, conflict detection, and PR metadata generation.

Breaks down complex coding tasks into subtasks and distributes them across multiple AI agents running in parallel, with a coordinator agent managing task dependencies, merging results, and handling inter-agent communication. Uses a DAG (directed acyclic graph) or state machine to model task dependencies, allowing agents to work on independent code modules simultaneously. Agents communicate via shared state (K8s ConfigMaps, etcd) or message queues to coordinate changes.

Automatically extracts and injects relevant code context (imports, dependencies, existing patterns, file structure) into agent prompts before code generation, enabling agents to generate code that follows repository conventions and integrates seamlessly with existing code. Uses code indexing (AST parsing, semantic analysis) to identify relevant files, dependencies, and patterns. Supports multiple languages and build systems (Python, JavaScript, Go, Java, etc.) to extract context appropriately.

Provides real-time monitoring of agent execution including logs, metrics (token usage, latency, success rate), and K8s-level events (Pod status, resource usage, restarts). Integrates with standard observability tools (Prometheus, Grafana, ELK stack) via metrics export and log aggregation. Tracks agent state transitions, captures execution traces for debugging, and provides dashboards for visibility into agent health and performance.

Abstracts LLM provider selection (OpenAI, Anthropic, local models, etc.) and agent behavior configuration through declarative configuration (YAML/JSON), allowing operators to swap providers, adjust temperature/max-tokens, and customize agent prompts without code changes. Supports multiple LLM providers with unified interface, enabling cost optimization (switching to cheaper models) or capability optimization (using specialized models for specific tasks). Configuration is stored in K8s ConfigMaps for easy updates.

Automatically runs tests (unit tests, integration tests, linting, type checking) on generated code before submitting PRs, validating that changes don't break existing functionality or violate code quality standards. Integrates with build systems (Maven, Gradle, npm, pip, go build) and testing frameworks (pytest, Jest, JUnit, etc.) to run tests in isolated environments. Captures test results and includes them in PR description or blocks PR submission if tests fail.

Implements automatic retry mechanisms for failed agent executions with exponential backoff, circuit breakers for cascading failures, and fallback strategies (e.g., switching to different LLM provider or simpler model). Tracks failure reasons (API errors, timeouts, validation failures) and applies appropriate recovery strategies. Supports manual intervention points where operators can review failures and decide whether to retry, skip, or escalate.

Tracks LLM API costs (tokens, API calls) per agent, task, or project and enforces budget limits to prevent runaway costs. Integrates with LLM provider billing APIs or estimates costs based on token counts. Provides cost dashboards, alerts when approaching budget limits, and can block agent execution if budget is exceeded. Supports cost allocation across teams or projects for chargeback.