LLM Agents That Actually Ship: A Field Guide

01An agent is a workflow with a goal

The word 'agent' gets overused, but the underlying idea is simple: a system that perceives, plans, acts, and observes the result. The difference between a great agent and a frustrating one is not the model — it is how tightly you constrain the loop around it.

Our field guide starts with a blunt rule: the more freedom you give the model, the more work you create for yourself. Start with the narrowest tool set that solves the task and widen it only when the eval set says you need to.

02Design the loop before you write prompts

Every agent we ship has the same skeleton, and we recommend you copy it: a goal statement, a bounded tool list, a step budget (maximum turns), a stop condition, and an escalation path to a human. Write all five down before prompting anything.

• Bounded tools: an agent with three well-scoped tools outperforms one with fifteen.
• Step budget: cap iterations so runaway loops cannot burn tokens or cause damage.
• Human escalation: define exactly what the agent must never do without approval.
• Observability: log every step, tool call, and thought — debugging without it is archaeology.

03RAG is 80% of the work

Most agent failures trace back to retrieval, not reasoning. If the model cannot find the right context, no amount of clever prompting fixes it. Invest in chunking strategy, embedding quality, and re-ranking before you touch prompt engineering.

For enterprise knowledge bases, we typically run a hybrid of keyword and vector search, add metadata filters, and re-rank the top results with a cross-encoder. That combination reliably beats pure vector search on domain-specific jargon — and costs surprisingly little.

04Evaluating agents is a different sport

Traditional ML evaluation measures accuracy on fixed examples. Agent evaluation must measure whether the whole trajectory reached the goal — did it use the right tools in the right order, terminate within budget, and avoid forbidden actions?

Build a small library of realistic 'missions' with expected tool sequences, and score against them on every prompt or model change. Teams that skip this step are, frankly, operating on vibes.

• Score goal completion, tool-choice accuracy, and safety violations separately.
• Maintain adversarial cases: ambiguous requests, missing data, and malicious prompt-injection attempts.
• Re-run your regression set before every deploy; agent regressions are silent and subtle.