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Salesforce, Inc., is an American cloud-based software company headquartered in San Francisco, California. It provides applications focused on sales, customer service, marketing automation, e-commerce, analytics, artificial intelligence, agentic AI, and application development. Founded by former Oracle executive Marc Benioff in March 1999, Salesforce grew quickly, making its initial public offering in 2004. As of September 2025, Salesforce is the 61st largest company in the world by market cap wi

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Agentic AI in backend systems: where autonomy wins (and where it breaks)

Agentic AI is ready to run multi-step backend workflows, but it only pays off when you bound autonomy and design for reliability. Agentic workflows formalize goals, state, and guardrails around one or more agents, turning intelligent steps into governable processes; see this definition and separation of concerns from [Grid Dynamics](https://www.griddynamics.com/glossary/agentic-ai-workflows), alongside a 2026 outlook on role shifts and velocity gains in engineering from [CIO](https://www.cio.com/article/4134741/how-agentic-ai-will-reshape-engineering-workflows-in-2026.html) and broad enterprise adoption trends noted by [MIT Sloan](https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained?_bhlid=caff052790723feb70ab1b3cf4bb7f444325a746). A practical rule of thumb: keep deterministic pipelines when steps are known and latency/cost must be predictable, and reserve agentic discretion for conditional tool use and discovery-heavy tasks; the trade-offs on latency, cost tails, and debuggability are laid out clearly in this [DEV](https://dev.to/sashido/agentic-workflows-when-autonomy-pays-off-and-when-it-backfires-27b0) guide (with SashiDo positioned as an execution substrate for agent backends). On adoption, Anthropic’s GUI-first agent runner (Claude Cowork) lowers the terminal barrier versus Claude Code, making agentic execution more accessible to non-CLI users while preserving multi-step autonomy; see hands-on notes in this [Claude Cowork review](https://aimaker.substack.com/p/claude-cowork-review-agentic-ai-guide) and a starter [Claude Code tutorial](https://www.youtube.com/watch?v=3HVH2Iuplqo), then pair that with risk-aware design: a cautionary “escape hatch” post on agent hallucinated security findings from [OpenSeed](https://openseed.dev/blog/escape-hatch/?_bhlid=d9fa13d91427f4109e48e35ccdef3d78432c6497), a delegation framework from [arXiv](https://arxiv.org/abs/2602.11865?_bhlid=2dc341bb7ee1c74fef0d92657b7571d1d90f7eb), and staged rollouts to avoid operational disruption from [HackerNoon](https://hackernoon.com/how-to-integrate-ai-agents-into-your-business-without-disrupting-operations?source=rss).

calendar_today 2026-02-20
claude claude-code claude-cowork anthropic microsoft

Salesforce pauses Heroku as AI agents rise; adjust autoscaling and pipelines

Vendors are pivoting from traditional PaaS and CI/CD toward agentic platforms, with Salesforce halting new Heroku features and leaders touting AI agents, underscoring the need to rethink autoscaling and delivery flows. Salesforce put Heroku into sustaining engineering while prioritizing Agentforce [TechRadar](https://www.techradar.com/pro/salesforce-halts-development-of-new-features-for-heroku-cloud-ai-platform)[^1]; meanwhile, Databricks' CEO argues AI agents will render many SaaS apps irrelevant [WebProNews](https://www.webpronews.com/the-saas-sunset-why-databricks-ceo-believes-ai-agents-will-render-traditional-software-irrelevant/)[^2], echoing calls for agentic DevOps beyond classic CI/CD [HackerNoon](https://hackernoon.com/the-end-of-cicd-pipelines-the-dawn-of-agentic-devops?source=rss)[^3]. A real-world ECS/Grafana case study shows AI-heavy, I/O‑bound stacks can miss CPU-based autoscaling triggers, requiring new signals and tests [DEV](https://dev.to/shireen/understanding-aws-autoscaling-with-grafana-gl8)[^4]. [^1]: Confirms Salesforce halted new Heroku features and is prioritizing Agentforce. [^2]: Summarizes Databricks CEO’s thesis that AI agents will displace traditional SaaS. [^3]: Opinion piece advocating agentic DevOps supplanting conventional CI/CD pipelines. [^4]: Demonstrates ECS autoscaling pitfalls for I/O‑bound, LLM-integrated workloads using Grafana and k6.

calendar_today 2026-02-10
salesforce heroku agentforce databricks amazon-web-services