AI Infrastructure in 2025: A Retrospective

Over 2025, applied LLM systems continued their transition from demonstration to production, and the accompanying infrastructure matured correspondingly. This article reviews five developments that recurred across the year, each linked to primary sources, and closes with tentative expectations rather than predictions.

Recurring themes

Durable execution

A persistent theme was the inadequacy of in-memory state for long-running agentic workflows, and the corresponding adoption of durable-execution patterns derived from workflow engines such as Temporal [1]. The underlying technique— event sourcing, in which state is reconstructed from an append-only log—is long established; its relevance to AI stems from the high per-step cost of model calls, which raises the cost of restarting failed workflows.

Production memory systems

Memory systems including Mem0 [2], Letta/MemGPT [3], and Zep [4] moved from research artefacts toward production use, and the LoCoMo benchmark [5] provided a common instrument for evaluating long-term conversational memory. The distinction between retrieval over a static corpus and memory as an evolving, temporally structured store became clearer over the year.

Framework consolidation

Microsoft consolidated AutoGen and Semantic Kernel into a single Microsoft Agent Framework, described as the successor to both and reaching Release Candidate status for .NET and Python [6]. The consolidation signalled a shift in the agent ecosystem from exploratory libraries toward supported, ecosystem-integrated platforms—and renewed attention to the associated portability trade-offs.

Observability

Observability tooling for LLM systems matured, exemplified by Langfuse open-sourcing its remaining commercial features—including LLM-as-a-judge evaluations—under the MIT license in June 2025 [7]. Tracing, evaluation, and cost accounting increasingly became baseline expectations rather than optional additions.

LLM gateways

An intermediary gateway layer—LiteLLM, Portkey, Kong AI Gateway, and Bifrost, among others—consolidated failover, caching, rate limiting, and cost accounting between applications and providers [8]. Comparative performance claims in this area are predominantly vendor-reported and should be interpreted with their methodologies in view.

Architectural consolidation

A design principle that recurred across the above is the separation of concerns into distinct layers—agent logic, orchestration, serving, and observability—each with a single responsibility:

graph LR
    A[Agent logic] --> B[Orchestration]
    B --> C[Serving]
    A --> D[Observability]
    B --> D
    C --> D

Systems that conflated these concerns tended to be harder to operate and evaluate than those that composed specialised components.

A note on quantitative claims

Retrospectives of this kind frequently cite precise adoption rates, cost savings, and failure-rate reductions. Where such figures cannot be attributed to a primary source, they are omitted here; unattributed aggregate statistics about "teams" should be treated as anecdotal.

Tentative expectations for 2026

The following are offered as hypotheses, not forecasts:

1. Durable execution as a default. Durable-execution guarantees may become a standard expectation of serious agent frameworks rather than a differentiator.
2. Consolidation of memory systems. The current plurality of memory frameworks may narrow as evaluation benchmarks such as LoCoMo [5] mature.
3. On-device inference. Continued progress in quantisation and efficient runtimes may make locally hosted agents practical for privacy-sensitive settings.
4. Regulatory influence. Enforcement of frameworks such as the EU AI Act [9] may drive standardisation of audit logging, human-oversight mechanisms, and explainability.
5. Production multi-agent systems. Coordination patterns may move from experimentation toward production deployment, contingent on adequate orchestration and durability guarantees.

References

1. Temporal Technologies, "What is durable execution?" https://temporal.io/blog/what-is-durable-execution
2. Chhikara, P. et al., "Mem0," arXiv:2504.19413. https://arxiv.org/abs/2504.19413
3. Packer, C. et al., "MemGPT," arXiv:2310.08560. https://arxiv.org/abs/2310.08560
4. Rasmussen, P. et al., "Zep," arXiv:2501.13956. https://arxiv.org/abs/2501.13956
5. Maharana, A. et al., "Evaluating Very Long-Term Conversational Memory of LLM Agents" (LoCoMo), arXiv:2402.17753. https://arxiv.org/abs/2402.17753
6. Microsoft Agent Framework DevBlog. https://devblogs.microsoft.com/agent-framework/migrate-your-semantic-kernel-and-autogen-projects-to-microsoft-agent-framework-release-candidate/
7. Langfuse, "Open-sourcing all remaining product features under the MIT license" (2025-06-04). https://langfuse.com/changelog/2025-06-04-open-sourcing-langfuse
8. LiteLLM documentation. https://docs.litellm.ai/
9. European Commission, "AI Act." https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai