What's the difference between an agent and a tool call?

A tool call is one step — the model picks a function and gets a result. An agent is a system that plans across steps, retries when things fail, and decides when it's done. Most production 'AI features' are tool calls dressed up; agents are when you let the model own the loop.

How do you keep LLM cost and latency from spiraling?

Three levers: route cheaper models to easier sub-steps (at Delos every AI task resolves through a tiered model registry with per-tenant overrides), persist expensive outputs instead of regenerating them, and instrument every call so you find expensive paths instead of guessing. The cost curve is fixable; it's just usually nobody's job.

What does production observability for agents actually look like?

Per-turn traces with inputs, outputs, tool calls, and token spend; a way to replay any conversation deterministically; alerts on precision and recall drift against a held-out eval set. Without those three, you ship and pray.

How do you know if your AI is actually working in production?

You define what 'working' means before you ship — a golden eval set, a precision target, a cost ceiling — and you monitor against it weekly. AI without an eval set is a demo, not a product.

Applied AI & Agentic Systems

AI features, agents, and LLM systems that ship to production, not demos that die in staging.

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01Questions buyers ask

What people actually ask before they hire us.

What's the difference between an agent and a tool call?: A tool call is one step — the model picks a function and gets a result. An agent is a system that plans across steps, retries when things fail, and decides when it's done. Most production 'AI features' are tool calls dressed up; agents are when you let the model own the loop.
How do you keep LLM cost and latency from spiraling?: Three levers: route cheaper models to easier sub-steps (at Delos every AI task resolves through a tiered model registry with per-tenant overrides), persist expensive outputs instead of regenerating them, and instrument every call so you find expensive paths instead of guessing. The cost curve is fixable; it's just usually nobody's job.
What does production observability for agents actually look like?: Per-turn traces with inputs, outputs, tool calls, and token spend; a way to replay any conversation deterministically; alerts on precision and recall drift against a held-out eval set. Without those three, you ship and pray.
How do you know if your AI is actually working in production?: You define what 'working' means before you ship — a golden eval set, a precision target, a cost ceiling — and you monitor against it weekly. AI without an eval set is a demo, not a product.

02What we deliver

The shape of the work.

Capabilities

Agentic systems & multi-step orchestration
Retrieval & RAG architectures
LLM evaluation, cost & latency optimization
MLOps & production observability

How it goes

Week 1Discovery + eval plan
Your use case, your data, your constraints. We leave with a written eval plan, a cost ceiling, and a target precision.
Weeks 2–4First agent in production
Behind a feature flag. Real traffic, real evals, real cost data — no synthetic benchmarks.
Weeks 5–8Iterate against the eval set
Tune routing, add retrieval, harden failure modes. Precision climbs, cost drops, observability gets real.
Week 9+Hand-off or continue
Your team owns the system, or we keep operating it as your AI partner. Your call.

After launch

The agent is in production. Now it has to stay good.

Cadence: Weekly evals against the golden set; routing, retrieval and failure modes tuned in two-week sprints with a Friday review, the way Delos has run since launch.
Commercial shape: Fixed-fee milestones to the first production release, then a monthly retainer with a capacity commitment — the shape Delos moved to when its fixed-fee phase closed.
Reviews: A monthly cost and precision read-out against the ceiling and target set in week one; a quarterly business review on the same numbers.
Hand-off: Eval sets, traces, prompts and runbooks live in your repos and accounts from the first commit. Your team can take the system over at any point.

03Proof

One client. One result. Real numbers.

Delos

Enterprise AI shipped in 14 weeks · media + political intelligence in one platform · tiered model routing

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Flagship deep dive

See how we build with AI

The full picture: how our Agentic Development Workflow turns ideas into agents in production — the workflow, the toolchain, the case studies, and how to start this week.

See how we build with AI

04Related work