Tabnine, the developer of the original AI software coding assistance, announced the launch of Tabnine Agentic, a significant advancement in enterprise software development that enables teams to deliver faster while maintaining complete control over their code and context.

Building on Tabnine’s industry-leading core competencies, Tabnine Agentic represents the next evolutionary stage of AI-powered development – ​​autonomous coding partners that execute entire workflows, not just providing code suggestions or completions, all while being tailored to each company’s individual standards and security policies.

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Tabnine’s Org-Native Agents are powered by Tabnine’s Enterprise Context Engine and understand each organization’s repositories, tools, and policies to plan, execute, and validate multi-stage development tasks—including refactoring, debugging, and documentation—within the organization’s controlled environment.

“Trusted AI isn’t about training larger models, but about grounding them in a real-world context,” explains Eran Yahav, CTO of Tabnine. “Our Org-Native Agents, built on the Enterprise Context Engine, are specifically designed for enterprises and set the standard for the next phase of AI. The focus is not only on delivering more code faster, but also on ensuring a measurable ROI and uncompromising governance.”

A recent MIT/BCG study found that 95% of AI initiatives in companies fail to deliver a return on investment, not because of the AI ​​models themselves, but because of poor integration with existing systems. While generic AI tools are suitable for individuals, “they reach their limits in the enterprise because they don’t learn from or adapt to workflows,” Fortune reports .

Tabnine Agentic closes this gap with its Enterprise Context Engine, which encompasses everything from coding standards and source and log files to ticketing systems and more. With this engine at its core, Tabnine’s Org-Native Agents execute complete coding workflows securely and contextually.

Unlike tools that rely solely on static training data, Tabnine’s agents can leverage external systems and tools, instantly adapting to new codebases and policies without requiring retraining or redeployment. The engine combines vector, graphics, and agent-based retrieval techniques to interpret relationships between codebases, tools, and tickets. This allows Tabnine’s Org-Native agents to accurately and contextually analyze multi-step workflows.

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Advantages for companies

Through this comprehensive integration into a company’s existing ecosystem, Tabnine Agentic can provide the capabilities that companies need to scale GenAI responsibly and effectively, via:

  • Adaptability : Because Tabnine’s AI is based on the current organizational context rather than static training data, it automatically adapts to new codebases and policies – without requiring retraining or redeployment.
  • Autonomy : Agents plan, act, and repeat coding processes, allowing developers to focus on higher-value tasks such as design and problem-solving.
  • Governance : Centralized controls ensure the monitoring of permissions, usage, and context, thereby supporting auditability and compliance.
  • Contextual intelligence : A comprehensive understanding of internal repositories, ticketing systems, and coding guidelines delivers precise, context-aware results.
  • Flexibility in deployment : Available via SaaS, private VPC, on-premises or air-gapped deployments – all while adhering to the strictest enterprise security standards.

A unique pricing model

With Tabnine Agentic, Tabnine is also setting a new standard for fairness and transparency in pricing for artificial intelligence. Tabnine Agents are based on simple, usage-based pricing without hidden fees – this offers IT managers in companies clarity and predictability.

Unlike other pricing models in the industry, Tabnine does not act as an intermediary charging markups for LLM usage. Instead, customers choose their LLM and pay for its use plus a small monthly platform fee. When customers use an LLM through Tabnine, billing is on a per-use basis, with only a small processing fee. Because Tabnine’s Enterprise Context Engine increases the efficiency of LLM usage, these savings are passed directly on to customers.

The pricing model allows “companies to maintain control and full control over their LLMs, workflows and environments through customizable quota limits per team or company,” explains Eran.

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Source – GlobeNewswire

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