JetBrains AI Assistant

JetBrains AI Assistant: Hybrid Orchestration & Agentic Mastery

As of January 2026, JetBrains AI Assistant has fully transitioned to a Hybrid-First Architecture. It utilizes Mellum, JetBrains' proprietary LLM, for ultra-low-latency local tasks such as Next Edit Suggestions and basic code completion, while offloading complex agentic workflows to high-parameter models like Claude 4.5 Sonnet and GPT-5 📑. The system's backbone is the PSI (Program Structure Interface), which allows the AI to navigate code hierarchies with compiler-grade precision 📑.

Core Orchestration & Agentic Engine

The platform introduces dedicated agents for autonomous development cycles.

• Junie & Claude Agent: These agents can autonomously analyze tickets, plan changes across multiple modules, execute code, and run tests to verify integrity. They operate within a secure sandbox and leverage the Anthropic Agent SDK for reasoning 📑.
• Next Edit Suggestions (GA): A predictive engine powered by Mellum that anticipates your next logical edit anywhere in the file (additions, deletions, or refactorings) based on recent changes 📑.
• Model Context Protocol (MCP): Full production support for MCP, allowing users to connect their own documentation servers, SQL schemas, and internal APIs as tools for the AI Assistant 📑.

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Enterprise Security & Local Inference

Security protocols are designed for Zero-Trust environments.

• Offline Mode via Ollama/LM Studio: Developers can switch to local models (e.g., Qwen 2.5 Coder or Codestral) to ensure zero data egress for sensitive internal codebases 📑.
• .aiignore Governance: Comprehensive support for .aiignore files to strictly control which files, directories, or symbols can be processed by AI models 📑.
• Unified Subscription (Free/Pro/Ultimate): New 2026 tiering ensures that high-priority GPU credits and agentic features are scaled according to organizational needs 📑.

Evaluation Guidance

Technical teams should prioritize the following validation steps:

• PSI Depth vs. RAG: Benchmark the accuracy of symbol resolution in large monorepos to verify that PSI-based context outperforms standard vector-search retrieval 🧠.
• Agentic Fail-Safe Loop: Audit the reliability of Junie's "test-before-commit" loop to ensure that autonomous changes do not introduce regressions in CI/CD pipelines 📑.
• MCP Tool Latency: Measure the overhead introduced by remote MCP servers when agents perform high-frequency tool-calling against internal databases 🌑.
• Mellum Performance: Evaluate the latency and accuracy of the Next Edit Suggestions feature on standard developer workstations to determine the impact on coding flow 📑.