AI Workflow Automation Starter (RAG + Agents)

High-ROI DevOps Use Case Selection

Not every operational process benefits equally from AI automation. We start by identifying the DevOps use cases with the highest return: runbook retrieval during incidents, incident context enrichment from past events, deployment approval workflows, infrastructure change review, CI/CD optimisation, and ticket enrichment from historical data.

Selection is based on three criteria: frequency of the task, cost of doing it manually, and feasibility of automating it reliably with current models. We discard use cases where the error cost is high and human judgement cannot be removed from the loop, and we prioritise those where automation delivers measurable time savings within weeks.

The output is a ranked shortlist with effort estimates, expected outcomes, and data requirements for each candidate. This gives your team a clear basis for deciding what to build first and what to defer.

RAG Baseline for DevOps Knowledge

Retrieval-augmented generation turns your operational knowledge into a queryable resource, but only if the retrieval layer is built correctly. Fry Express delivers a RAG baseline with connectors to your DevOps knowledge stores -- infrastructure state, deployment history, incident records, runbooks, CI/CD configurations, and architecture decision records -- with a chunking and indexing strategy tuned to operational content.

Access control is not bolted on after the fact. The retrieval layer inherits permissions from the source systems so that a user or agent querying the system never sees records they would not have access to directly. This is enforced at the retrieval boundary, not at the prompt level.

Indexing is designed for relevance and maintainability. We configure incremental updates so that new deployments, resolved incidents, and updated runbooks are reflected without full re-indexing. Fry Express builds the RAG layer on open, portable components so you are not locked into a single vector database or embedding provider.

Agentic Workflow Integration Into DevOps Tools

An agent that cannot interact with your real systems is a demo, not a product. We wire agentic workflows into the DevOps tools your teams already use: CI/CD pipelines, monitoring and alerting platforms, ticketing systems, code repositories, and infrastructure management tools.

Integration is scoped and controlled. Each agent has a defined set of actions it can perform, and actions with operational side effects -- triggering a deployment, modifying infrastructure, or closing an incident -- require human-in-the-loop approval before execution. An agent can draft a rollback plan or propose a ticket update, but a human confirms before the change is committed.

Fry Express configures the orchestration layer to support multi-step workflows where agents chain actions across DevOps systems. Each step is logged with full context so that the decision path is auditable and the approval flow integrates into existing tools without creating a new bottleneck.

Operational Quality Evaluations and Regression Gates

Agent performance degrades silently if it is not measured continuously. We deliver quality evaluations and regression gates that test agent outputs against known-good baselines on every release and on a recurring schedule.

Evaluations are framed around operational correctness: did the agent propose the right runbook? Did it correctly correlate the alert with the deployment that caused it? Did the infrastructure change review catch the misconfiguration? Results are tracked over time, and regression gates in CI block deployment when a model update, prompt change, or data shift causes a quality drop.

These agents run on the DevOps platform foundation and extend it into daily operational workflows -- automation your team trusts, your compliance function can audit, and your finance team can track.