> Markdown alternate of https://steerscale.com/services/ai-systems-engineering

SVC-01 · Engineering service

# AI Systems Engineering

Build and operate LLM and agent systems as production software — with evaluation, observability, reliability and economics designed in.

[Discuss an AI system](https://steerscale.com/contact) [Start with an audit](https://steerscale.com/services/ai-systems-audit)

\[Figure: Illustrative production LLM request path: gateway, router choosing between a small and a large model, tool calls with a retry budget, evaluation gate and tracing across every step.\]

Capabilities

## The parts that turn a demo into a service.

-   01
    
    ### Agents & orchestration
    
    Bounded loops, explicit state, step budgets, idempotent tool calls and recovery paths that hold under concurrency.
    
-   02
    
    ### RAG & model integration
    
    Retrieval pipelines, context budgeting, caching and provider abstraction without hiding failure behaviour.
    
-   03
    
    ### Model routing & economics
    
    Route by task, not by habit. Cost per successful workflow measured and attributed to customer and feature.
    
-   04
    
    ### Evaluation & release gates
    
    Representative datasets, regression suites, quality/cost/latency thresholds, canaries and rollback for prompts and models.
    
-   05
    
    ### Observability
    
    One trace per request across models, tools and retries, carrying tokens, cost, versions, tenant and outcome.
    
-   06
    
    ### Resilience & hardening
    
    Timeouts, backoff with jitter, rate-limit handling, provider fallback, queueing and load behaviour verified before launch.
    

Typical engagements

## Where this work usually starts.

Most engineering engagements follow a [AI Systems Audit](https://steerscale.com/services/ai-systems-audit), so the work is scoped from evidence rather than a wish list.

1.  01
    
    Implementing audit remediation
    
    Routing, retry policy, tracing and evaluation gates from a prioritized findings list.
    
2.  02
    
    Taking an agent from pilot to production
    
    State, concurrency, failure handling and telemetry added before real traffic arrives.
    
3.  03
    
    Model migration with a safety net
    
    Replay harness, thresholds, canary and rollback so a provider or model change is a controlled release.
    
4.  04
    
    Embedded senior engineering
    
    A defined window in your repo and standups, with written handoff at the end.
    

## Operating an LLM or agent system that needs to become boring?

Describe the workflow and where it hurts. If an audit is the better first step, we will say so.

[Discuss an AI system](https://steerscale.com/contact)