Building reliable LLM agent workflows

Generative models are now capable of more than answering questions - they can plan, route, and act. Yet the gap between a promising prototype and a dependable production agent remains wide. At REMILINK, we engineer LLM platforms and agentic workflows with the same rigor as any mission-critical system: policy-first design, measurable quality gates, and resilient operations.

Principles we use to ship safely

• Intent clarity first: codify what the system is allowed to do, for whom, and under which constraints before choosing a model.
• Guarded autonomy: pair tool-calling agents with policy libraries, sandboxing, and escalation paths so automation never outruns safety.
• Observable by default: log decisions, inputs, and tool results with trace identifiers to enable replay, debugging, and compliance.
• Evaluation as code: treat prompts, routing logic, and flows as versioned artifacts with CI pipelines and regression suites.

A reference WBS for a production agent

Below is a condensed work breakdown structure we use when delivering agentic systems. It removes any product-specific details but shows the rigor needed to reach reliability:

1. Environment & governance: repo policies, branching and merge rules, coding standards, and secure secret handling; CI with baseline CD.
2. Intent detection & routing: design intents (search, clarification, chat), craft system instructions, and route requests accordingly.
3. Rewriter module: validate user input, normalize abbreviations and typos, handle slang, unify languages, and expand queries when beneficial.
4. Entity extraction: isolate critical entities (IDs, categories, attributes) for downstream state tracking.
5. State management: maintain conversational memory, track entity states, and implement clarification loops with explicit handoffs.
6. Data preparation: collect and normalize catalog data, categories, and cross-references needed for retrieval.
7. Semantic search: select and evaluate embedders, preprocess knowledge graphs, compare embeddings, and define ambiguity handling policies.
8. Guardrails: integrate safety layers, author policy specifications, and defend against hallucinations.
9. Response formulation: assemble results, propose human handoff when confidence drops, and align messaging tone.
10. Quality & evaluation: build test sets, automate metrics, and run regression harnesses for every model, prompt, and policy change.

Those principles translate into disciplined delivery patterns: we define intents and policies up front, build guarded tool-calling flows, instrument everything for replay and evaluation, and treat prompts plus routing logic as first-class, versioned code.

How this shows up in delivery

In recent engagements, these practices produced agentic systems that triage complex requests, enrich them with retrieval, and call internal tools without compromising safety. Key ingredients include:

• Multi-agent topologies with a dedicated router, a rewriter, and task-specific actors.
• Policy packs that govern tool access, PII handling, and escalation thresholds.
• Offline and online evaluation harnesses that score intent accuracy, extraction quality, tool success, and user satisfaction.
• Roll-forward/rollback playbooks with telemetry for latency, cost, and failure modes.

Where to start

We typically begin with a two- to four-week feasibility sprint that establishes intents, designs the agent topology, sets guardrails, and builds an evaluation harness. From there, we iterate through instrumented pilots that validate safety, quality, and ROI before scaling.