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Over the past year, federal agencies gained broad access to enterprise AI through the OneGov initiative, at prices unlike any normal software deal. The current OneGov portal lists OpenAI ChatGPT Enterprise at $1 per agency, Anthropic Claude at $1 per seat, and Google Gemini for Government at $0.47 per agency. Those introductory offers begin expiring on September 30, 2026, the final day of fiscal year (FY) 26, which places renewal squarely in the FY27 planning cycle.

At Datadog, we want to expose our engineers to high-quality AI tools and workflows. However, token usage can be expensive, and finding a balance between AI cloud spend and the return on investment can be difficult. But what if engineers could maintain their current AI workflows using the same tools, but at a lower cost?

Agents are already calling your self-service APIs, querying your catalog, and independently provisioning resources around the clock. According to Gartner’s 2026 Hype Cycle for Agentic AI report, agents have had the most aggressive adoption curve of any emerging technology. Gartner even considers agents to be a formal user persona, referring to the agent experience (AX) throughout the report.

Microsoft’s Azure Functions serverless compute service abstracts away infrastructure management to simplify how teams deploy and operate applications. However, the managed environment can make it harder to understand what happens inside those applications after deployment. Traditional approaches that rely on agents, extensions, and sidecars for direct collection of telemetry data aren’t available across every Azure Functions hosting plan, which can leave gaps in end-to-end visibility.

OpenTelemetry (OTel) tail-based sampling helps teams control trace volume by retaining errors, slow requests, and other traces worth investigating while dropping lower-value traffic. In distributed systems, a single request can fan out across many services, each emitting spans. That volume adds up quickly. Some applications produce millions of traces per hour, while large clusters generate more than 10 billion spans per day.

Mature experimentation programs eventually have to report the cumulative impact of their shipped changes. The request might come as an ROI story for leadership, a revenue update for finance, or a gut check on the quarter’s progress. The tempting shortcut is to sum the observed lift from each winning experiment and report the total. That naive sum almost always overstates the truth because of a statistical artifact called the winner’s curse.

Teams often track operational work across spreadsheets, Slack threads, Jira tickets, and whatever system generated the original alert or signal. This fragmentation makes it difficult to maintain a consistent record of what needs attention, who or what is addressing the issue, and what has already happened. As AI agents take on more responsibility for investigations, triage, and code changes, the number of handoffs grows, making ownership, status, and history even harder to preserve.

AWS Lambda durable functions let you build long-running, multi-step workflows for use cases such as payment processing, order fulfillment, and AI workflows with human approval. A single durable execution can pause for a wait or callback, retry failed work, and resume in a fresh Lambda invocation without losing its state. The strong resilience provided by durable executions, however, creates an observability challenge because each invocation produces its own telemetry data.

Data pipelines are systems for moving and processing data. They are made up of concatenated services and data stores that programmatically ingest data from upstream sources; filter, transform, enrich, and route that data; and deliver it to downstream consumers.

An account can show signs of disengagement long before a renewal conversation begins. Users may stop returning to a core workflow, stall during onboarding, or skip a newly released feature. Product teams often see these signals only at the user level, while annual recurring revenue (ARR), plan, renewal date, and ownership data remain in a customer relationship management (CRM) system or data warehouse.