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📊 KPMG Q3’25: AI Agents Move from Experimentation to Deployment

• 42% of organizations report they’ve deployed AI agents (up from ~11% in Q1).

• 55% are currently piloting agents. Experimentation (just exploring) has dropped sharply.

• “Build & buy hybrid” is now the preferred strategy: 57% of firms say they are both developing in-house and purchasing third-party agent solutions.

Key Shifts & Why They Matter

• Risk and oversight ramping up: More firms are insisting on human-in-the-loop oversight (61%, up from ~45%) and limiting agent access to sensitive data without checks. That signals maturity — people are no longer just building, but asking how safely as well as how fast.

• Data & cybersecurity more urgent than ever: The biggest barriers now are data quality (82% citing it vs 56% last quarter) and cybersecurity (78% up from ~68%). Problems that may have seemed secondary earlier are now front of mind.

• Workforce & hiring are adapting: Resistance from employees has dropped significantly (from ~47% to ~21%), while companies are investing more in prompt engineering, adaptability, continuous learning, and domain-specific skillsets. Also, 56% expect entry-level hiring strategies to shift within the next year.

What to Watch Out For

• Measuring ROI differently: Traditional metrics are increasingly seen as inadequate. Leaders are under pressure from boards & investors to capture value in a way that aligns with how AI delivers it. But many still expect measurable ROI within the next 12 months.

• Agentic complexity is real: As systems get more capable, their complexity nearly doubled according to respondents. Complexity brings fragility: scaling, unexpected edge cases, monitoring problems. Strong architecture, observability, and error-handling will be vital.

• Trust, fairness & public perception: Concerns about misuse are rising. Trust in accuracy/fairness of outputs has dipped somewhat. For AI agents to be sustainable at scale, organizations will need to invest not just in technical risk mitigation, but also in transparency and governance.

What You Should Do Now

1. Accelerate pilot → deployment: If you’re still exploring, pick one agentic use-case, deploy it end-to-end, and learn from real users.

2. Invest heavily in data foundations: Clean, well-labeled, accessible data is no longer optional. Data quality underpins whether agents fail or succeed.

3. Define clear oversight & mandate policies: Decide who reviews agent decisions, when human intervention is required, and what sensitive data agents can access.

4. Update hiring & training plans: Prioritize hiring people with prompt-engineering skills, domain expertise, adaptability. Upskill existing teams in working with agents.

5. Rethink ROI frameworks: Build metrics for impact that capture agent-specific benefits (time savings, error reduction, insight generation) beyond just revenue or headcount.

🔍 Silicon Valley Doubles-Down on RL Environments to Train Stronger Agents

A new wave of investment is fueling simulated workspaces (aka RL environments) as the preferred way to train AI agents that can actually do things, not just respond. These environments let agents practice multi-step, tool-using workflows in controlled, interactive settings where they get rewards and penalties. Big AI labs (OpenAI, Anthropic, Meta) are building their own, while startups like Mechanize, Prime Intellect, Mercor, Surge, etc., are emerging as specialist providers.

Why This Shift Matters

• Closing the gap on real tasks: Agents often fail when facing realistic, multi-step workflows (e.g. navigating messy UIs, backtracking when something breaks). RL environments simulate those challenges so agents can learn strategies, persistence, error recovery, etc., not just pattern matching.

• Infrastructure as a differentiator: Just as labeled datasets were the backbone for the previous AI surge, high-quality RL environments are becoming foundational infrastructure for the next phase. They’re expensive, complex, and not just about scale — fidelity, diversity, feedback mechanisms all matter.

• Democratization + competition: There’s a push to provide environments not just for large labs but also for smaller developers. Think “Hugging Face for RL environments” (Prime Intellect), domain-specific vendors (Mercor), etc. That could lower the barrier for teams to develop agents for specialized uses.

What to Watch Out For

• Reward hacking & unintended behavior: When incentives aren’t perfectly aligned, agents may “game” the reward system instead of solving the intended task. Robust design and evaluation are key.

• Cost, compute, and maintenance overhead: Building high-fidelity simulated workspaces—capturing UI quirks, software bugs, unpredictable behavior—requires massive engineering investment, compute power, and ongoing updating. Not all teams can sustain that.

• Generalization & sim-to-real gap: Environments may not cover every edge case or mirror production systems perfectly. Agents trained in simulation still need careful testing and adaptation when deployed in the wild. Standards, benchmarks, and shared evaluation will matter.

What Your Team Should Consider Doing Now

1. Evaluate whether your agent use-cases need RL environments — particularly if you require workflows spanning multiple tools, user input, UI interaction, handling unexpected states.

2. Partner early with environment providers or invest internally in building smaller, high-fidelity simulation rigs for your domain (e.g. enterprise software, regulatory contexts, healthcare).

3. Design for safety & oversight from the start — reward functions, escalation paths for failure, human monitoring — because simulated training amplifies potential scale of mistakes.

4. Benchmark & test across environments — don’t just trust performance in one simulator. Try a variety of scenarios, stress test edge cases.

5. Invest in modular, maintainable environment design — as tools/web UIs change, environments will need frequent updates; building them cleanly will save future pain.

🛒 Amazon’s 24/7 AI Agent for Sellers

Amazon has upgraded its Seller Assistant into a proactive, always-on AI agent for third-party sellers. Powered by Amazon Bedrock (using models like Amazon Nova and Anthropic Claude), it can monitor performance, flag inventory issues, suggest compliance actions, and even take approved actions automatically — all while giving sellers control over permissions. Instead of waiting for you to check dashboards, it now works around the clock to:

• Monitor inventory & demand

• Flag compliance/account risks early

• Suggest (or auto-take) actions like pricing, promos, restocking

👉 See Amazon’s full Seller Agent rollout details →

Why it matters: Freed up from constant dashboard-watching, sellers can reduce losses (slow stock, compliance fines), move faster on marketing & ads, and focus more on growing rather than managing. It’s especially helpful for smaller sellers with limited teams.

What to watch out for: Too much automation without review can lead to mistaken actions (e.g. wrong compliance fixes, mispriced items). Keep permissions tight. Expect rollout to begin in the U.S., with global expansion coming later.

👉 Read how sellers are setting up permissions + first results →

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