INTELLIGENCE
Give the agent a job it can actually finish.
We build agents for work that's too judgment-heavy to script and too repetitive to keep paying a senior person to do by hand. An agent reads a situation, chooses among real options, and acts, inside limits you set and can see. When a task isn't ready for one, we'll say so.
THE PROBLEM
You've already tried this. It didn't stick.
Most agentic AI programs fail quietly, after the demo goes well. Here's what that failure looks like from the inside:
- A pilot agent impressed a room and never touched a real customer, ticket, or transaction, because nobody built the part that makes a demo survive production traffic.
- Analysts still manually stitch together four or five systems for a routine judgment call the agent was supposed to own, because the agent was never given the tool access to do it end to end.
- The agent works fine until it meets the case nobody tested. Then it fails silently, or worse, answers confidently and wrong, and nobody downstream notices until a customer does.
- Nobody can say, in one sentence, what the agent is allowed to do on its own, what it costs per completed task, or who is accountable when it gets one wrong.
WHAT WE BUILD
Six things, and we won't sell you the ones you don't need.
Task-scoped agents
Agents built around one bounded job, such as triage a queue, draft a response, reconcile two systems, or summarize a case file, not a general-purpose assistant that tries to do everything and answers for nothing.
Tool and retrieval integration
Typed tool schemas connect the agent to your actual systems: internal APIs, ticketing, document stores, databases. That's what lets it act instead of just talking about acting.
Orchestration and multi-agent workflows
Supervisor/worker patterns, hand-offs between specialized agents, and state machines for processes that span more than one step or one system.
Memory and context management
Session memory for a single interaction, longer-term retrieval for context that should persist, and deliberate limits on both so the agent doesn't drag in more than it needs to decide well.
Evaluation harnesses
Offline evaluation suites and golden datasets run before every prompt, tool, or model change ships, turning 'it seemed to work in testing' into a measurement instead of a feeling.
Guardrails and cost control
Output validation, human-approval gates on consequential actions, per-task spend caps, and a kill switch. These are the parts every rushed pilot skips.
HOW WE ENGAGE
Five phases. The first one might end with 'don't build this.'
- 1–2 weeks
Fit Assessment
Agent Candidate Brief: a written go/no-go recommendation naming the specific task, the judgment boundary, and, when warranted, an explicit recommendation not to build an agent at all
- 2–3 weeks
Design & Guardrails
Task Specification & Tool Contract: exact tool access, escalation rules, and the evaluation plan the build will be held to
- 4–8 weeks
Build & Eval
- A working agent scored against the golden dataset
- An Eval Scorecard and cost-per-task model, produced before anyone sees it in production
- 4–6 weeks
Guarded Pilot
Shadow-mode or approval-gated production rollout, with an incident log reviewed weekly
- ongoing
Scale & Operate
A monitoring dashboard, drift/eval regression alerts, and a monthly review with the process owner
TECHNOLOGY
The stack, plainly: we're model-agnostic by design.
We choose models and frameworks per task, not per contract. As of 2026 our default stack looks like this:
- Models
- Claude Opus 5 for the highest-judgment reasoning steps; Claude Sonnet 5 as the default workhorse for most production agents; Claude Haiku 4.5 for high-volume, low-latency, cost-sensitive steps. We also work in GPT-5.x and Gemini 3 where a client's existing platform or procurement already commits them there.
- Agent frameworks & orchestration
- LangGraph, the Claude Agent SDK, the OpenAI Agents SDK, CrewAI, and AutoGen/AG2 for multi-agent patterns; Temporal or Vercel Workflow underneath for durable, resumable execution when a task spans minutes or days rather than seconds.
- Tool use & retrieval
- Vercel AI SDK and native provider tool-calling for typed tool schemas; pgvector, Pinecone, or Weaviate for retrieval; direct API integration into the client's own systems wherever one already exists. We don't add an agent step in front of an API a script could call directly.
- Evaluation
- Braintrust, LangSmith, promptfoo, and Anthropic's own evaluation tooling for offline scorecards, regression testing, and golden-dataset management.
- Guardrails & routing
- Guardrails AI or custom output validators; Vercel AI Gateway or OpenRouter for provider routing, fallback, and cost observability across models.
GOVERNANCE & RISK
The part enterprise buyers actually need to read.
An agent that can act is a system that can act wrongly. We govern that risk the same way we'd expect a bank or a hospital to:
- Human-approval gates on any action above a defined stakes threshold: refunds, external communication, irreversible writes.
- A full audit log of every tool call, input, and decision the agent makes, retained on your schedule, queryable by incident.
- Model risk review plugs into whatever model-risk or vendor-risk framework you already run. We don't ask you to invent a new one for us.
- Data handling terms that default to no training on client data, with retention and residency terms set per engagement.
- Per-task and per-day spend caps, rate limits, and a manual kill switch reachable by the process owner, not just by us.
- Adversarial and red-team testing against the agent's tool permissions before any production go-live.
- A written 'when not to use an agent' decision, produced in Phase 1 and revisited at every review, because the honest governance artifact is sometimes the one that stops a build.
QUESTIONS
What to ask before you greenlight an agent.
RELATED SERVICES
Agents need somewhere to stand.
An agent is only as good as the data it reads and the platform it runs on. Three services usually show up alongside this one: