Building agents for revenue teams taught us the model matters less than the context
Summary
Rimplo's founders share design lessons from building an agent platform for revenue teams, arguing that context quality, vertical specificity, and human approval matter more than which LLM is used.
Similar Articles
The boring parts of sales agents matter more than the model
A practitioner shares hard-won lessons from building AI sales agents, arguing that reliability depends more on plumbing — email verification, fast inbound replies, verifiable step completion, and hard guardrails — than on the underlying model.
Building agents taught me the model is rarely the problem. What's your hard-won lesson?
A developer shares hard-won lessons from building AI agents: focusing on tool design over model choice, using small loops instead of giant prompts, logging agent context, adding guardrails early, and creating small evals to catch bugs.
Has anyone else found that context matters more than model size for AI agents?
The author shares their experience building AI agents, finding that providing clear context and guidance (defining job, rules, tools) matters more than model size for reducing mistakes and improving performance.
Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP
A controlled study of compound LLM agent design in an adversarial POMDP (CybORG CAGE-2), systematically varying context, reasoning, and hierarchy across five model families. Key findings: programmatic state abstraction yields large returns per token, hierarchy without deliberation tools achieves best absolute performance, and context engineering is more cost-effective than deeper reasoning.
We built a revenue agent. The hard part was stopping a plausible draft from becoming a bad action.
Building a revenue agent revealed that the hardest challenge is preventing plausible but flawed outputs from translating into real-world actions.