AI agents that ship — and stay shipped.
Agent demos are easy. Agent reliability is hard. We build evaluated, observable agents that complete real tasks in production — with the guardrails, evals, and human-in-the-loop fallbacks that demos skip.
Most agents work in a demo. Few work in production.
Someone builds an agent that handles a happy-path task in a notebook, the team gets excited, and six months later it's still in pilot — because as soon as it meets real data, the success rate craters from 95% to 40%.
The reasons are predictable, and they're not about the model. They're about everything around the model: tool reliability, memory boundaries, error handling, eval coverage, observability, and designing for failure modes instead of demos.
Six classes of agents we ship into production.
Single-purpose task agents
One agent, one tool surface, one job — research, summarization, classification, extraction.
Multi-agent systems
Coordinator + specialists. An orchestrator delegates to focused worker agents.
Browser-using agents
Navigate, click, fill forms, extract from web UIs. Playwright, Browserbase, computer-use.
Voice agents
Inbound/outbound phone agents on LiveKit, Vapi, Retell. Support, qualification, scheduling.
Customer-support copilots
Tier-1 agents with KB retrieval, ticket triage, escalation, tone control.
Internal ops agents
Database queries, ETL, finance reconciliation, RevOps. Boring, valuable, high-ROI.
Evals before agents.
Most teams build the agent first and figure out evals later. We do it backwards.
Define success, in writing
Concrete, measurable rubrics for what 'the agent did the task correctly' means.
Build the golden dataset
50–500 real examples with correct answers. The harness everything runs against.
Build against the harness
Every prompt iteration, model swap, and tool change runs the full eval. Regressions block merge.
Shadow production traffic
Run in shadow mode against real traffic before serving users. Diff against humans, then promote.
What “production-ready” actually requires.
- Tool input/output schemas with runtime validationPydantic / Zod on every tool boundary, with structured errors the agent can recover from.
- Retry, fallback, and circuit breakersEvery external call has a retry policy and a fallback. Repeated failures circuit-break and escalate.
- Human-in-the-loop checkpointsHigh-stakes actions pause and ask a human. Configurable per workflow.
- Cost and latency budgetsHard cost ceilings and latency targets. Runaway agents are killed and logged.
- Observability with trace IDsEvery LLM call, tool call, and decision tagged. Production failures are replayable.
- Eval regression gates on deployCI runs the golden-dataset eval on every commit. Regressions block deploy.
How agent projects engage with us.
Agents are a build + tune + observe loop. Our engagements are sized accordingly.
- Discovery + agent design
- Golden dataset + baseline eval
- Prototype + honest go/no-go
- Full eval harness + golden dataset
- Architecture, tools, guardrails, observability
- Shadow rollout + 30-day support
- Weekly eval review + dataset expansion
- Model regression testing on new releases
- New tools + on-call incident response
Common questions.
What success rate should I expect?
How do you measure agent quality?
Single agent or multi-agent?
What models do you use?
Self-hosted or hosted models?
What happens when a model gets deprecated?
Tell us what you want the agent to do.
Bring us the task. We'll tell you honestly whether it's agent-shaped, whether it's eval-ready, and what the right engagement looks like.