AI workflows - Elastic: Hope-flows and hard truths
This is a guest post by Nic Palmer, head of customer architecture for international, Elastic – the company known for its open source platform that powers search, observability, security and wider functions.
Palmer writes in full as follows…
AI workflows are critical to modern enterprises. Yet they often over-index on the model layer. If there’s failure, it usually stems from weak plumbing rather than weak models.
A failure in AI workflow may be a consequence of a ‘hope-flow’.
If you can’t see, control, or adapt a workflow from end-to-end, you can’t expect it to be resilient. When something breaks at 3am on a holiday, you don’t want to be stuck running on hope.
Teams can waste hours troubleshooting, projects stalland decisions may be made on untrusted data, creating frustration and operational risk. Fixing a process where errors can come from anywhere is no small task.
The antidote to a ‘hope-flow’ is solid foundations: monitor, control, understand and act with trust in the process.
What makes AI workflows brittle
Critical areas of failure in AI workflows stem from poor data architecture quality, schemaand governance challenges. The rule still holds: garbage in, garbage out.
The second weak spot is over-engineering big, flashy use cases instead of starting small. The pressure to launch sophisticated AI projects quickly often leads teams to bite off more than they can chew, generating early setbacks and scepticism. It’s better to launch carefully, build confidence with pilotsand grow at a pace the team can handle.
The third common failure comes from a fundamental lack of monitoring, observability and appropriate guardrails. This can create a wider pushback on AI, slowing adoption. Leaders at almost all firms now have AI projects on their docket, but ill-defined projects may cause blowback.
Strong AI workflows aren’t just about clever models. They need solid foundations. Three pillars really matter.
First: hybrid search
You can’t act on AI if you can’t find the right context. Hybrid search, powered by RAG, combines retrieval with generation to surface relevant data while controlling token use, costand exposure.
Feed the right sourcesand RAG stops workflows from being wildcards. Instead it turns them into reliable assistants that you can trust in high-stakes situations. Less hallucinations, less noiseand more confidence for engineers.
Second: observability
If you can’t see it, you can’t fix it. End-to-end visibility – latency, data movement, context window growth – lets you catch issues before they cascade. Observability also surfaces how humans and AI interact, so you can make sure automation actually helps decision-making rather than complicating it.
For security-conscious teams, this is gold: auditing what goes into AI, monitoring data and token flows, spotting unusual usage patterns. Observability turns unknown risks into something you can act on. You can’t protect what you can’t see.
Third: security, governance & education
Guardrails such as IP boundaries, cost controls and misuse prevention are essential. But this goes beyond code. Any AI use can introduce risk, especially when handling sensitive data.
Tools like SAST/DAST help keep AI-assisted code close to best practices, but policies and education are just as critical. Teams need to know what’s safe to share, when it’s okay to use AI and how to interpret outputs responsibly.
Observability ties all of this together, giving security and ops teams the context they need to audit interactions and maintain trust. When you combine visibility, governance and strong security, AI workflows behave reliably and engineers can deploy them with confidence.
Solid foundations, not blind hope
Resilient AI workflows aren’t built on clever models alone. They depend on solid foundations that provide visibility, context and control. Start small. Establish baselines in search, observability and security. Scale with confidence. Focus on the outcome.
These technologies form the base for relevance, resilience and trust. They are the pillars that keep AI workflows reliable when it matters most. At 3 am on a rainy bank holiday, engineers must rely on workflows that follow the same path a human would: consulting documentation, interpreting signalsand offering clear next steps.
Combine human judgment with robust AI support. Act quickly. Make accurate decisions. Reduce errors. When you invest in solid processes, you will see stronger outcomes, higher engagement and a flywheel effect that accelerates future projects.
The payoff is tangible: faster time to insight, fewer errors and reduced operational friction. That four-month ROI shows you why solid foundations deliver lasting results.