The Anyshift Blog.

Two customer investigations show how a context graph was key to helping agents retrieve the right data by tracing changes and shared dependencies.

Roxane Fischer

Sep 25, 2026 · 5 min read

During a production investigation, an agent must connect applications, machines and alerts. Across five integrations and ten tasks, Anyshift graph queries used 83% fewer model tokens and took 47% less time on average than native MCPs.

Anyshift

Sep 23, 2026 · 7 min read

We rebuilt our agent's architecture to separate operational knowledge, model runtime, and side effects. When we added OpenAI Codex support, the pull request only touched one file

Pierre Chollet

Sep 3, 2026 · 7 min read

Unified production context makes AI agents cheaper, faster, and easier to scale. In our live benchmark, resolving relationships before inference cut model-token usage by 83.5%, while reducing response time and estimated cost without changing the task outcome.

Roxane Fischer

Aug 17, 2026 · 7 min read

We tested six models on a bounded task: turn a plain-English infrastructure question into one supported query. Deterministic normalization and execution erased most output differences, leaving Haiku and Luna slightly ahead of Opus on final-answer accuracy. Luna then matched Haiku at 21% of the observed cost.

Stephane Jourdan

Aug 12, 2026 · 9 min read

Annie (our AI SRE agent) had institutional memory from ACE, the agentic context-engineering loop that curates cheatsheets from past runs. It worked, but clients kept catching her trusting stale entries or missing answers buried in her own bloated context. So we added five things on top: (1) a fixed set of memory items always presented to the agent, (2) per-query retrieval over the rest of the memory store, (3) an agent-optimized index of that store, (4) the ability for the agent to query the store mid-run, and (5) tried-and-true memory freshness mechanisms. Production context, now optimized by the AI using it. Here's the reasoning and what a few weeks in production say.

Ghazi Felhi

Jun 10, 2026 · 10 min read

An AI agent in the incident channel can run kubectl and read a dashboard. What it can't do is judge whether the last deploy is the suspect or a red herring. We open-sourced the SRE skills that encode that judgment, runnable offline against fixtures with no credentials.

Louis Fradin

Jun 9, 2026 · 4 min read

Private equity firms are pushing AI across portfolios, but the hard part is no longer experimentation. It is turning sponsor-level AI pressure into governed, production-aware operating workflows that actually move EBITDA.

Roxane Fischer

Jun 2, 2026 · 8 min read

The 10 best AI SRE tools in 2026 compared by architecture, root cause analysis, remediation, and change awareness — from Anyshift's versioned graph to Resolve AI's autonomous agents.

Roxane Fischer

Mar 13, 2026 · 15 min read

AI agents start every run from scratch. ACE (Agentic Context Engineering) gives them institutional memory that evolves through use, cutting root cause analysis time by 30%.

Ghazi Felhi

Mar 11, 2026 · 8 min read

The limits of telemetry-only AI approaches to SRE and why topology is the missing piece.

Roxane Fischer

Jan 27, 2026 · 8 min read

GenAI is everywhere. But very often, the cool and exciting demos don't work the same way in production.

Roxane Fischer

Oct 15, 2024 · 7 min read