Jet AI Agents Explained: JetBrains Air, Ava & the 2026 Workflow Automation Revolution
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- JetBrains launched 'Air,' a multi-agent coding IDE, and 'Central,' an enterprise governance platform, in March 2026 — fundamentally changing how dev teams collaborate and automate work.
- Jet.AI's 'Ava' agent launched December 24, 2024, booking private jets via SMS — a clear signal that vertical SaaS industries are going fully agentic.
- Gartner predicts 40% of enterprise applications will feature task-specific AI agents by end of 2026, up from less than 5% in 2025, with a 1,445% surge in multi-agent system inquiries logged between Q1 2024 and Q2 2025.
- The global AI agent market crossed $7.6 billion in 2025 and is projected to exceed $50 billion by 2030 — making agent-ready business tools a strategic priority, not a nice-to-have.
What Happened
Two companies with "Jet" in their names made headlines in the AI agent space over the past 18 months, and both tell an important story about where business tools are heading.
First, there's Jet.AI (NASDAQ: JTAI) — originally a private aviation company that launched an AI booking agent called "Ava" on December 24, 2024. Ava lets customers text or call a dedicated number (+1-888-492-4538) to check real-time aircraft availability, get pricing, and book charter flights — no app download, no long hold times, just a conversation. However, Jet.AI made a dramatic strategic pivot: on February 14, 2025, the company announced it sold its entire aviation business to flyExclusive in an all-stock deal worth up to $22 million. The company is now repositioning itself as an AI data center infrastructure provider, targeting $1 million in revenue per megawatt with a planned 50-megawatt facility scalable to 1 gigawatt. As the CEO wrote in a shareholder letter, the company is moving "from aviation divestiture to AI data center investment" — betting on infrastructure for the AI economy rather than end-user applications.
Second — and more immediately relevant for small business owners and remote teams — is JetBrains, the software company behind popular developer tools like IntelliJ IDEA. In March 2026, JetBrains launched "Air," a public preview of an agentic IDE (a coding environment where AI agents do the work autonomously, not just assist). Air lets multiple AI agents — including Codex, Claude Agent, Gemini CLI, and JetBrains' own Junie — run at the same time in isolated Docker containers (self-contained virtual environments that keep each agent's work separate) and Git worktrees (independent copies of your codebase). Then on March 24, 2026, JetBrains introduced "Central," an enterprise platform that unifies governance, cloud agent infrastructure, and shared context across all your team's code repositories — and in doing so, retired its older 'Code With Me' pair-programming tool.
Why It Matters for Your Team's Productivity
The shift from AI copilots to AI agents is the single biggest change in productivity software in 2026 — and understanding it is essential whether or not your team writes a single line of code.
Here's a plain-English way to think about it. An AI copilot is like a very smart assistant who reads your documents and suggests what to write next. An AI agent is like an assistant who actually opens the document, writes the content, schedules the follow-up, files the report, and sends the notification — all without you approving each individual step. That distinction matters enormously for workflow automation.
According to a JetBrains survey of 11,000 developers conducted in January 2026, 90% now use AI at work. More importantly, 22% are actively using AI coding agents, and 66% of companies plan to adopt agents within the next 12 months. If your business relies on any software development — even outsourced — these numbers signal a fast-changing landscape for your vendors, your timelines, and your quality expectations.
For non-technical teams, the picture is equally striking. IDC expects AI copilots to be embedded in nearly 80% of enterprise workplace applications by 2026. Microsoft has publicly called 2026 "the year of the agent." Gartner logged a 1,445% surge in multi-agent system (systems where multiple AI agents coordinate to complete complex tasks) inquiries from Q1 2024 to Q2 2025 — and now predicts that 40% of enterprise applications will feature task-specific AI agents by end of 2026, up from less than 5% in 2025. The best SaaS tools entering the market aren't just adding an AI chat widget; they're being rebuilt from the ground up around agents that can act, verify their own work, and self-correct.
JetBrains Central illustrates what this looks like for team collaboration in practice. Rather than each developer operating their own isolated AI assistant, Central creates a shared system where agents carry full context about your codebase and can be governed at the team or enterprise level. JetBrains described it as connecting "developer tools, agents, and development infrastructure into a unified system where automated work can be executed and governed across teams and tools — with no vendor lock-in." For small businesses concerned about being tied to a single platform, that vendor-neutral design is a meaningful differentiator among competing business tools.
The global AI agent market reflects this momentum: it crossed $7.6 billion in 2025 and is projected to exceed $50 billion by 2030. The productivity software landscape of 2028 will look very different from today — and the teams that start building agent-fluent workflows now will have a measurable head start.
The AI Angle
Building on that market momentum, two foundational protocol shifts made the current wave of AI agents technically possible — and they're worth knowing if you're evaluating any new productivity software or business tools.
Anthropic released the Model Context Protocol (MCP) in late 2024 — think of it as a universal plug adapter that lets AI agents connect to external tools, databases, and apps in a standardized way. Before MCP, every integration required custom code. With it, a single agent can read a file, update a CRM record, send a message, and log a task — all in one automated workflow. Google followed with the Agent-to-Agent (A2A) Protocol, which allows agents from different vendors to communicate directly with each other.
JetBrains Air is a direct product of these standards — it runs Codex, Claude Agent, and Gemini CLI side by side in the same pipeline, something that was nearly impossible before cross-vendor protocols existed. For teams evaluating workflow automation tools today, MCP and A2A compatibility are reliable signals that a tool can integrate into a broader agent ecosystem rather than locking you into one vendor's closed system. Tools built on open protocols tend to age better and connect more naturally to the team collaboration and business tools you already use.
What Should You Do? 3 Action Steps
Before adopting new AI agents, take stock of the productivity software your team already relies on. Does your project management tool, CRM, or communication platform have an API (a way for two apps to share data and trigger actions) or MCP integration? Tools that support these connections will slot into multi-agent workflows most naturally. Start with your top three to five tools, check their integration documentation, and flag any that are entirely closed systems — those may become bottlenecks as your workflow automation matures.
Rather than rolling out AI agents company-wide, pick one repetitive workflow in a single department — customer support ticket triage, code review, invoice matching, or social media scheduling — and test an agent-enabled tool there first. JetBrains Air, for example, is currently in public preview, meaning dev teams can experiment without a full financial commitment. Define clear success metrics before you start: time saved per week, error rate reduction, and team satisfaction scores. A 30-day pilot with honest data is far more valuable than a broad rollout based on vendor demos.
One reason JetBrains Central generated attention is that it addressed governance — the policies that control what agents can access, what they're allowed to do, and who's accountable when something goes wrong. Before your team scales up agent usage across your business tools, define three things: which data sources can agents read or write to, who reviews agent-generated outputs before they reach customers or stakeholders, and how actions are logged for audit purposes. Establishing these guardrails early prevents costly errors and builds the internal trust needed to expand agent use across more of your team collaboration workflows.
Frequently Asked Questions
How is JetBrains Air different from GitHub Copilot for small dev teams in 2026?
GitHub Copilot is primarily a code suggestion tool — it assists one developer at a time with in-line recommendations. JetBrains Air goes further by running multiple AI agents simultaneously, including Codex, Claude Agent, Gemini CLI, and JetBrains' own Junie, each in isolated Docker containers (self-contained environments that prevent conflicts between agents). That means different agents can work on separate parts of your codebase at the same time, compressing timelines for small teams with limited bandwidth. Air also connects to JetBrains Central for enterprise-level governance and shared semantic context — a layer of team collaboration infrastructure that Copilot doesn't yet offer at the same depth of cross-agent coordination.
Is Jet.AI's Ava booking agent still available for private jet charters after the company's 2025 pivot?
Jet.AI launched Ava on December 24, 2024, as an SMS and phone-based charter booking agent (+1-888-492-4538). However, on February 14, 2025, Jet.AI sold its entire aviation operations to flyExclusive in an all-stock deal worth up to $22 million and pivoted to become an AI data center infrastructure company. Whether Ava continues as a consumer-facing booking service under flyExclusive's brand, or has been integrated into their own business tools and systems, has not been publicly confirmed as of April 2026. If you're looking for AI-assisted private charter booking, verify current service availability directly with flyExclusive before making plans.
What is the Model Context Protocol (MCP) and why does it matter for choosing workflow automation tools?
The Model Context Protocol — MCP for short — is an open standard released by Anthropic in late 2024. Think of it as a universal connector that lets AI agents plug into external apps, databases, and services in a standardized way. Before MCP, building an agent that could interact with multiple tools required custom integrations for every connection, which was expensive and fragile. With MCP, a single agent can query a database, update a project board, send a Slack notification, and generate a report — all as part of one workflow automation sequence. When evaluating any new productivity software that claims to be "agentic," checking for MCP compatibility is one of the clearest ways to distinguish genuinely capable tools from rebranded chatbots.
Are AI coding agents like JetBrains Air worth it for small businesses that mostly outsource their development work?
Even if you outsource most development, AI coding agents affect you because your vendors are almost certainly already using them. A JetBrains survey of 11,000 developers in January 2026 found that 90% use AI at work and 22% actively use AI coding agents. In practice, you may start seeing faster delivery timelines from outsourced teams — but also new questions about which agent generated which code and how it was reviewed by a human. Asking your development partners about their AI governance policies (what gets human review, what's fully automated, and how outputs are tested) is a reasonable and increasingly necessary question when evaluating business tools and vendor relationships in 2026.
How do I tell whether a SaaS tool's AI agent features are genuinely useful or just marketing hype in 2026?
The clearest signal is whether the tool's agent can actually take action — not just suggest it. A real agent reads inputs, makes decisions, executes multi-step tasks, and reports results without requiring human approval at every step. Ask vendors three specific questions: First, does the agent support MCP or A2A protocols, or is it a closed proprietary system? Second, can it handle a complete workflow end-to-end, or does it hand off to a human at each stage? Third, is there a full audit trail — a log of every action the agent took, with reasoning? Among the best SaaS tools on the market today, those that answer yes to all three are genuinely agentic. Tools that struggle to answer clearly are likely offering copilot-style assistance rebadged as agents, which is a meaningful difference for any team relying on real workflow automation.
Disclaimer: This article is for informational purposes only. Tool features and pricing may change. Always verify current details on the official website.
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