01: The problem
Most "AI agents" are demos that fall apart the day a real user touches them.
Your team built an impressive prototype. It worked beautifully in the demo. Then you tried to ship it, and the agent started hallucinating tool calls, looping on edge cases, racking up API bills, and failing silently when the model got upgraded.
The problem isn't the agent framework. It's everything you didn't build around it: the evals, the kill switches, the cost controls, the observability, the permissions, the rollback plan when the agent does something it shouldn't. That's the gap between an agent that wows a board meeting and an agent your customers depend on.
We close that gap. Every agent we ship has the unglamorous infrastructure that keeps it running in production, measurable behavior, controllable costs, and an off switch that actually works.
02: Right fit?
Is this the right fit?
Best for
- ✓SaaS or ops teams replacing manual work with agents (support triage, data processing, workflow execution)
- ✓Product teams adding agentic features inside their app (autonomous task execution, multi-step workflows)
- ✓Founders who tried building an agent in-house and watched it fall over in production
- ✓Teams who need accountability, measurable agent behavior, not "trust the model"
Probably not
- —Pure chatbot use cases, if it's "answer questions in chat," you don't need an agent (start with our AI Integration service)
- —Single-shot LLM calls (summarize this, classify that), a function call is enough
- —"Let's see what an agent can do" without a concrete task or rollout plan
- —Use cases where one bad agent action causes irreversible damage and you have no appetite for human-in-the-loop
03: What you get
What you get when we ship
Every agent we build is observable, evaluable, controllable, and reversible. Not a demo. Not an experiment. A production system you can put in front of customers or rely on internally.
01The agent itself
- Single-agent or multi-agent orchestration (whichever fits the problem)
- Tool use / function calling, connected to your existing APIs, databases, and services
- Memory and state management, short-term context, long-term persistence
- Planning, reasoning, and self-correction patterns
- Human-in-the-loop where it matters; full autonomy where it's safe
02Evals & accuracy
- An eval suite that tests agent behavior on real cases before every deploy
- Regression tests so model upgrades don't silently break the agent
- Quality scores per task, per user, per cohort
- Adversarial tests, what happens when the input is malformed, malicious, or weird
03Production controls
- Kill switch, instant disable from your dashboard, no redeploy required
- Per-tenant cost caps, rate limits, and circuit breakers
- Permissions and guardrails, agent can only touch what it's allowed to
- Audit logs of every action the agent took, every tool it called, every output it produced
04Observability
- Latency, cost, accuracy, drift, measured per agent, per task, per user
- Trace every agent run end-to-end (LangSmith, Langfuse)
- Alerts when behavior changes
- Dashboards your ops team can actually read
04: How we work
The Production Path
Every engagement runs on the same 4-phase delivery system. No surprises. No "discovery phase that never ends." No hourly invoices.
Phase 1
Discover
1 week
A paid Scoping Sprint. We define the agent's scope of action, map the tools it needs, design the eval set, and deliver a fixed quote for the build. Ends with a go/no-go.
Phase 2
Build
4–10 weeks
We ship in weekly milestones. The agent runs against real data in staging from week one. Evals run on every commit. You watch the agent get measurably better, week by week.
Phase 3
Harden
1–2 weeks
Adversarial testing, cost controls, kill switch, monitoring, audit logs. We don't call it shipped until you can deploy it, monitor it, and shut it down, without our help.
Phase 4
Operate
30 days included, retainer optional
Post-launch monitoring included for 30 days. After that, optional retainer for eval maintenance, cost optimization, model upgrades, and new agent capabilities, most clients move into one once the first agent is live and proven.
05: Stack
The stack we ship on
We pick the best-of-breed agent stack, and the boring, battle-tested infrastructure around it.
- Agent frameworks
- LangGraph, LangChain, CrewAI, Vercel AI SDK, OpenAI Agents SDK Custom orchestration when off-the-shelf doesn't fit
- Models
- Claude (Sonnet, Opus), usually the right choice for complex reasoning GPT-4o, GPT-4 Turbo for specific tasks Open-source (Llama, Mistral) when self-hosting matters Model selection per agent step, small models where they're enough, big ones where they're needed
- Tool use & integrations
- Function calling against your APIs, databases, and services MCP (Model Context Protocol) servers for standardized tool access Webhook, queue, and event-driven patterns for async agent work
- Memory & state
- PostgreSQL, Redis for short-term state pgvector, Pinecone, Qdrant for long-term semantic memory Custom memory architectures (episodic, semantic, working) when needed
- Evals & observability
- LangSmith, Langfuse, RAGAS, Braintrust Custom eval suites tied to your real-world cases Sentry, Datadog, OpenTelemetry for runtime monitoring
- Production controls
- LaunchDarkly, Unleash, GrowthBook for kill switches and gradual rollout Per-tenant rate limiting and cost caps Audit logging to your data warehouse
06: Why us
Why we'll still be here when you launch
The Production Guarantee
If your agent doesn't ship, we don't invoice the final milestone. We're accountable for production, not hours.
You own everything
Code, prompts, evals, model choices, infrastructure. Hosted on your accounts. No license fees. No vendor lock-in. You can fire us and take the whole agent tomorrow.
Methodology in the open
The full Production Path is published. Phase by phase, what you get, what we need from you, exit criteria. The only thing we don't publish is pricing, that's quoted after scoping.
NDA standard
Every engagement is covered by an NDA by default. Mutual NDA on request before the scoping call.
07: FAQ
Frequently asked
What's the difference between an agent and a chatbot?+
A chatbot answers questions. An agent takes actions. If your AI needs to call APIs, update databases, run workflows, or make decisions that affect your business, you need an agent. If it just needs to chat, an integration is simpler and cheaper.
How long does a typical agent take to build?+
4–12 weeks for the production build, plus a 1-week Scoping Sprint up front. Single-agent systems with a small tool set land in 4–6 weeks. Multi-agent orchestrations with broad tool access land in 8–12. The exact timeline lands in the fixed quote after scoping.
What about hallucinations and weird behavior?+
This is what evals are for. Every agent we ship has an eval suite tied to real-world cases, and the suite runs on every code change and every model upgrade. If the agent's behavior degrades, we catch it before deploy. If something slips through, the kill switch is one click away.
Can the agent run autonomously, or does it need human approval?+
Both. We design the human-in-the-loop boundary based on the cost of a wrong action. Low-stakes actions (categorize a ticket) run fully autonomous. High-stakes actions (issue a refund, send an email to a customer) require approval. You decide the boundary on the scoping call.
Which models do you use?+
Whichever fits the use case. Claude is usually our default for complex reasoning. GPT-4o for specific strengths (vision, structured outputs). Open-source (Llama, Mistral) when self-hosting matters or costs need to come down. We pick per agent step, small models where they're enough, big ones where they're needed.
How do you control runaway costs?+
Per-tenant budgets, per-task caps, rate limiting, circuit breakers. Alerts on spikes. Dashboards on day one. We size the model to the task, and we use the smallest model that delivers the required quality. Cost optimization is part of the eval suite, if a cheaper model passes the evals, we use it.
Can you work with our existing development team?+
Yes, frequently. Many of our agentic AI engagements are joint with the client's in-house team. We document properly, write code that reads like yours, and hand off cleanly.
What happens if the model provider deprecates the model we're using?+
Built into the design from day one. Every agent has the eval suite that proves it works. When a new model drops, we run the suite, if it passes, we upgrade; if it fails, we stay on the previous version. No surprise breakage.