If your enterprise deployed Amazon Q Business, Kendra, or Bedrock Agents in the past two years—congratulations, you were an early adopter. AWS has now put all three services into maintenance mode. Capability development stopped June 30. New customers stopped being accepted July 30-31. And the successor services AWS is pointing you toward are materially different products, not simple upgrades. You have a migration decision to make, and the clock is already running.
This isn't a routine cloud service update. This is AWS retiring AI services faster than many enterprises complete a single procurement cycle. Bedrock Agents launched in November 2023. Q Business launched roughly a year later. AWS is now moving both into maintenance mode before most enterprises have finished deploying them at scale.
Understanding what happened, why AWS made this move, and what it means for your roadmap is the right place to start. Then we can talk about the 90-day plan.
What AWS Actually Did on June 30
The announcement, framed as a "service availability update," moved roughly 20 services and features into maintenance mode in a single day. The three headline names are Q Business, Kendra, and Bedrock Agents—but the full scope is broader.
Ten Amazon SageMaker AI features also moved to maintenance in the same update, including Ground Truth, Clarify, Debugger, and Model Monitor. A March 2026 update had already pushed AWS App Runner, CloudTrail Lake, and Audit Manager into maintenance, while Amazon WorkMail and RDS Custom for Oracle moved to full sunset. Taken together, these represent the most coordinated catalog pruning AWS has executed.
In AWS terminology, maintenance mode sits between full support and sunset. Existing customers keep their workloads running. AWS continues security patches and bug fixes. Feature development stops entirely, and new customers cannot sign up. There is no hard end date announced for existing workloads—yet.
The specific timelines: Kendra stopped accepting new customers July 30. Q Business closed to new customers July 31. Both remain available to existing deployments indefinitely under the current guidance, but "indefinitely" in maintenance mode is a planning risk, not a commitment.
The Successor Map: What Replaces What
AWS has been deliberate about the consolidation narrative. Every retired service has a designated successor, and the successor map reveals the strategy.
Kendra → Bedrock Knowledge Bases. Amazon's managed RAG (retrieval-augmented generation) service with built-in connectors, hybrid search, and an agentic retrieval API becomes the replacement for the enterprise search service. The architectural direction is sound—RAG-native retrieval is more aligned with how enterprises want to deploy AI than pure semantic search. But the migration is not clean. Some Kendra data source connectors lack a native equivalent in Bedrock Knowledge Bases, and AWS's own migration guide recommends routing unsupported sources through Amazon S3. That means additional infrastructure, additional complexity, and additional engineering time.
Q Business → Amazon Quick. The enterprise AI assistant platform that AWS launched is being folded into Amazon Quick, the platform AWS introduced in October 2025 by merging Q Business capabilities with Amazon QuickSight. Quick is a broader product—it includes Quick Flows for workflow automation, QuickSight integration for structured data analysis, Research for in-depth research, and Spaces for unified knowledge management. The vision is compelling. The migration involves rebuilding connector configurations and adapting application integrations.
Bedrock Agents Classic → Bedrock AgentCore. The agent execution platform gets a rename and a successor. AgentCore is AWS's new foundation for running production agents, positioned as more robust for the kind of multi-step, tool-calling agent workflows that enterprises are increasingly deploying. The "Classic" label on the old Bedrock Agents is AWS's way of telling you it's the legacy path.
Why AWS Made This Move Now
The why matters more than the what for long-term planning.
AWS built Q Business, Kendra, and Bedrock Agents as separate products during a period when no one was sure how enterprise AI would be consumed. The result was a sprawl of overlapping point solutions—each solving a slice of the problem independently. Microsoft and Google arrived at consolidation earlier. Microsoft folded its enterprise AI capabilities under the Copilot brand. Google consolidated under Gemini Enterprise. Both built unified experiences from the start of the current generation.
AWS shipped first-generation products and is now unwinding them in public. The resulting consolidation—Bedrock for models and retrieval, AgentCore for agent execution, Quick Suite for business user experience—is architecturally cleaner than what it replaces. But the timing creates a real problem for enterprises that made platform decisions in 2023 and 2024 based on AWS's product roadmap signals.
The deeper dynamic is competitive pressure. AWS's enterprise AI market share has faced headwinds from Microsoft's Copilot momentum and Google's Gemini Enterprise integration. Maintaining a fragmented catalog of point solutions makes it harder to compete against unified platforms. Consolidation is the right long-term call—but it comes at the cost of enterprise buyers who standardized on the first generation.
For Business Leaders: The Vendor Risk Calculation
The business case for early adoption of enterprise AI has always rested on moving faster than competitors. AWS actively encouraged enterprises to deploy Q Business, Kendra, and Bedrock Agents with re:Invent launches, case studies, and dedicated migration playbooks. Enterprises that followed that guidance are now absorbing migration costs that weren't in the original business case.
This creates a specific vendor risk calculation worth internalizing before the next cloud AI platform decision.
The cost of migration is not just engineering hours. It's the opportunity cost of roadmap capacity redirected from innovation to infrastructure maintenance. An enterprise that planned a 2026 initiative to expand its AI-powered workflow automation now has a competing priority: migrating the foundation that initiative is built on. That reallocation of engineering attention has real business impact, even if it doesn't show up on a migration cost estimate.
The budget implication for business leaders: migration to Quick Suite and AgentCore should be treated as unplanned capital expenditure. AWS's migration guidance is comprehensive, but "phased approach starting with Bring Your Own Index" is not a synonym for "minimal effort." Budget for discovery (inventorying what's deployed and how), architecture (designing the target state), migration execution, and testing. Organizations with significant Q Business or Kendra deployments should plan for months of engineering work, not weeks.
The procurement lesson is structural. Cloud AI services should be evaluated on more than current capability. A service that launched 24 months ago and is already in maintenance mode is evidence that cloud AI catalogs are actively rotating. Future procurement decisions should include abstraction requirements—applications should not be written against specific AWS service APIs without an isolation layer that enables migration.
For Technical Leaders: The Migration Architecture
The migration complexity varies significantly by how deeply your applications are coupled to the deprecated service APIs.
For Kendra deployments, the critical variable is connector coverage. Bedrock Knowledge Bases supports a large set of data source connectors natively, but gaps exist. AWS recommends routing unsupported sources through S3 as an intermediate step. If your Kendra deployment relies on connectors that aren't natively supported by Bedrock Knowledge Bases, you're looking at a two-phase migration: first to an S3-based intermediary, then to native connectors as they become available. Map your connector dependencies before estimating migration scope.
For Q Business deployments, the complexity depends on how you integrated Q Business into your applications. Customers using Q Business for anonymous access and API integration into custom applications face the most friction—AWS explicitly recommends contacting AWS Support to discuss custom migration approaches for these deployments. The standard migration path uses Model Context Protocol (MCP) integrations for connectors that Quick Suite doesn't natively support, but those MCP integrations cannot serve as knowledge base data sources for document indexing. That's a meaningful architectural constraint.
For Bedrock Agents deployments, the migration to AgentCore is the most fluid of the three. AWS has positioned AgentCore as a direct evolution rather than a complete redesign. But fluid doesn't mean automatic—agent configurations, tool definitions, and orchestration logic need to be validated against AgentCore's execution model before assuming compatibility.
AWS's migration guidance mentions an automatic migration path scheduled for Q4 2026. That option exists, but treating "automatic migration in Q4" as your plan means letting AWS make architectural decisions for you under a compressed timeline. It's not a recommended approach for production workloads.
The 90-Day Migration Plan
The absence of a hard sunset date creates a false sense of time. Use the window constructively.
Days 1-30: Discovery and risk assessment. Inventory every production workload that touches Q Business, Kendra, or Bedrock Agents Classic. Classify each by migration complexity: simple (well-abstracted integration), moderate (some service-specific API dependencies), and complex (deep coupling or unsupported connectors). For complex workloads, engage AWS Support immediately—the custom migration paths require early coordination.
Days 31-60: Architecture and planning. Design the target-state architecture for each workload. For Kendra migrations, validate connector coverage against Bedrock Knowledge Bases before committing to a migration timeline. For Q Business migrations, decide whether to use the BYOI (Bring Your Own Index) path to accelerate user and app migration while deferring data source migration. For Bedrock Agents migrations, test agent configurations in AgentCore in a staging environment.
Days 61-90: Pilot migration. Migrate one lower-complexity workload end to end. Validate performance, functionality, and cost against the baseline. Use the pilot to calibrate estimates for remaining workloads and to surface gaps in your migration plan before they become production incidents. Document what worked and what didn't.
Beyond day 90, execute remaining migrations with the benefit of real migration data rather than estimates. The goal is to be off maintenance-mode services before AWS sets a hard sunset date—because when that date is announced, you'll want your migration already done, not starting.
The Abstraction Principle Going Forward
The broader lesson from this wave of AWS retirements is one that applies to every cloud AI platform decision from this point forward.
Enterprises that built applications with clean abstraction layers between business logic and cloud service APIs will have migration paths measured in weeks. Enterprises that wrote directly against Kendra's API, Q Business's API, or Bedrock Agents' API without an isolation layer will have migrations measured in months.
The principle is not new—it's the same argument that has been made about database portability and messaging system lock-in for decades. But the speed of cloud AI catalog rotation makes it more urgent than it has ever been in enterprise software. AWS shipped Bedrock Agents in November 2023. It's in maintenance mode as of June 2026. That's 31 months from launch to end of feature development.
Any enterprise AI application that cannot migrate to a different underlying service within a reasonable engineering sprint is carrying technical debt that compounds with every platform decision. The abstraction layer is not an architecture nicety. It's the hedge against catalog rotation—and the AWS June 2026 update is proof that rotation is accelerating.
What AWS Is Betting On
The consolidated platform AWS is pointing enterprises toward is better than what it replaces. Bedrock Knowledge Bases is more capable than Kendra as an AI-era retrieval system. Amazon Quick is more ambitious than Q Business as an enterprise AI assistant. AgentCore is a more production-hardened execution environment than Bedrock Agents Classic.
If AWS holds this consolidated architecture through the next two re:Invent cycles, the June pruning will read as a calculated strategic bet that traded short-term migration pain for long-term competitive positioning against Microsoft and Google. Enterprises that align their roadmaps with Bedrock, AgentCore, and Quick Suite now will carry less migration debt going forward.
The question is whether AWS maintains commitment to the current generation for long enough to justify the migration investment. The last two years of AWS AI service history suggest the answer requires a hedge, not a bet.
What to Do This Week
The immediate actions are straightforward. Pull the list of production workloads that touch Q Business, Kendra, or Bedrock Agents Classic. Assign engineering owners to each. Flag the ones with complex connector or API dependencies for early AWS Support engagement. Brief your business stakeholders on the unplanned migration scope.
The services are not going dark tomorrow. But maintenance mode is a one-way door—no new features, no new customers, and a sunset date that will eventually be set. The enterprises that treat this week as the start of a structured migration program will have options. The ones that wait for the hard end date will have deadlines.
AWS's June 2026 service availability update is a case study in the velocity of cloud AI platform evolution. The enterprises best positioned to absorb it are the ones that built for portability from the start. The ones most exposed are the ones who trusted that a product launched at re:Invent had a multi-year roadmap. Both lessons apply to every cloud AI decision you're making right now.