Data analysis over the past month has revealed a fundamental structural shift in the IT industry. Big Tech companies are no longer thinking in terms of disjointed tools. Autonomous platforms, where security, observability, and resource management are baked in at the core level, are coming to the forefront. Having analyzed dozens of releases and incidents, we have identified three macro-trends that we believe will dictate architectural decisions over the next 6–12 months.
Trend 1: From APIs to operating systems for AI agents
Artificial intelligence is rapidly outgrowing the format of simple stateless calls (request-response). Today, AI is a full-fledged runtime platform requiring state management, isolated sandboxes for secure execution, and complex distributed observability.
Market analysis shows that corporate focus is shifting to orchestration. The emergence of long-lived execution environments (like Project Think) and centralized registries like the AWS Agent Registry confirms this: AI agents are becoming full-fledged infrastructure entities, replacing single LLM calls with complex hierarchical supervisor-worker models. And this approach is already proving its economic efficiency in practice. The implementation of the internal agent Honk at Spotify saved the company 10 engineering weeks, and STCLab’s HolmesGPT system reduced incident investigation time from 20 minutes to 2 minutes.
What remains in the past: The concept of “LLM as an API” without lifecycle management, and exclusively manual SRE runbooks.
Trend 2: Security as a foundational infrastructure primitive
Information security has finally ceased to be an overlay added after application deployment. It is becoming an architectural primitive built directly into compute, data pipelines, and the execution environment.
A radical strengthening of security models is being observed everywhere. Kubernetes is introducing isolation via User Namespaces and fine-grained API authorization. Static secrets are being replaced by the concept of Workload Identity Federation, as seen in the updated Vault 2.0. Simultaneously, AI is forging a new threat vector, scaling attacks on CI/CD vulnerabilities. Pressure from compliance, particularly the new EU Cyber Resilience Act requirements for mandatory SBOM generation, is only accelerating the transition to jurisdiction-sensitive architectures (sovereign fault domains) and strict isolation of confidential containers.
What remains in the past: Perimeter security, strictly post-factum scanning, and access management without Identity federation.
Trend 3: Cost-Performance rebalancing
The era of “scaling at any cost” is over. Against the backdrop of globally rising infrastructure costs, the industry is transitioning to rigorous systemic optimization at all levels: from machine learning models to data storage and monitoring.
We see a clear shift toward quantization and local deployment of smaller AI models. In database architecture, engineers are revisiting fundamental trade-offs. For example, shifting the focus from heavy sharding to smart replication allowed ScyllaDB-based projects to cut costs by 50% compared to DynamoDB. Infrastructure efficiency is becoming the primary KPI: thus, introducing the WebSocket protocol in high-load systems yields up to a 40% improvement in latency at 1000 TPS, while internal optimizations at Datadog accelerated query processing from 300 milliseconds to an incredible 38 microseconds. The “slimming down” trend has even touched monorepos—engineers at Dropbox managed to compress their monorepo from 87 GB to 20 GB.
What remains in the past: Overprovisioning (allocating excess resources “just in case”) and blind reliance on managed clouds without detailed cost transparency.
Takeaways for engineers: what to prepare for?
The industry is moving toward self-regulating systems where AI agents, security, and infrastructure merge into a single managed layer. The winners will not be the teams with access to the best language model, but those who build a reliable “operating system” to operate it.
- Build Agent-ready platforms. Implement stateful execution and memory. Set up deep observability for AI agents, capturing not just metrics, but reasoning traces.
- Switch to Identity-First security. Abandon static passwords in favor of Workload Identity. Implement policy-as-code (Kyverno, OPA) at the runtime level and strictly isolate the permissions of AI tools in sandboxes.
- Optimize architecture for FinOps. Evaluate all technical decisions through the “cost per outcome” metric. Separate hot/cold data paths and use compact local models for routine tasks.
- Invest in Platform Engineering. Standardize “golden paths” for development through internal developer platforms (IDP) to radically reduce the cognitive load on product teams.