Most agent demos crash at step seven. The internet is full of CrewAI gifs that work once and never again. Production agents need step budgets, tool schemas that actually work, error recovery, audit trails, and an eval suite that catches when a model upgrade breaks them. We build that kind. With reliability you can ship.
$22/hr
Senior agent engineer
5 days
Free agent PoC
90%+
Typical eval pass rate at GA
Tell us the goal and the tools. We'll match a senior agent engineer in 24 hours.
NDA-friendly · Replies in 4 hours
Six patterns that cover ~90% of real-world agent work. We'll tell you which one matches your problem and which one to avoid.
One LLM, 5–20 tools, planning loop. Best for support copilots, sales research, internal Q&A. Ships in 2–3 weeks. The right answer 80% of the time.
Specialized agents with handoffs — researcher, writer, reviewer; or planner, executor, critic. CrewAI or LangGraph. Better quality on complex tasks at higher latency and cost.
LangGraph state machine with explicit nodes, edges, checkpoints. Survives restarts, supports human approval gates, replayable. Best for long-running business processes.
Playwright + vision model, Anthropic Computer Use API, or Browser Use. For automating web workflows that have no API. Sandboxed, audited, kill-switched.
Real-time voice agent on Twilio, Vapi, LiveKit, or Retell with sub-second latency, tool calls to your CRM, and full transcripts piped to evaluation.
Agent execution generates training signal — failures become eval cases, successful trajectories become few-shot examples, and weekly DPO updates the policy model.
Framework-fluent and framework-skeptical. We'll use whatever ships fastest with the right reliability properties.
We resist the temptation to multi-agent everything. Most problems want one agent with great tools — and we'll tell you when.
We map the human workflow first — steps, decisions, tools, handoffs, irreversibility points. Decide single-agent vs multi-agent vs explicit workflow.
Working agent end-to-end on 3–5 real scenarios, with tracing in LangSmith and a starter eval suite. You see traces, costs, and failure modes.
Add tool schemas, error recovery, budgets, audit logs, human approval gates, eval coverage. Ship behind a feature flag, monitor real users.
Re-eval on every model upgrade, expand the test scenarios from production failures, tune tool descriptions as the agent learns to use them better.
Three engagement models. Free PoC because agent reliability is the only thing worth measuring.
End-to-end agent
Free
One working agent on your real workflow, traces visible in LangSmith, starter eval suite. No commitment.
6–14 weeks
$20K – $200K
Hardened agent or multi-agent system with eval, observability, audit, budgets, and human-in-the-loop. Fixed scope.
Monthly
$22 – $36/hr
Senior agent engineer embedded with your team. Best for evolving products with a long agent roadmap.
We've shipped enough agents to know what breaks them — and we engineer around it from day one.
Step, tool, and cost budgets are baked in. Your agent can’t accidentally spend $5K on Sonnet calls.
LangSmith / Braintrust traces from the first commit. You see every decision the agent makes.
Tool schemas with examples, error messages designed for the model, dry-run modes for irreversible actions.
We’ll talk you out of a 7-agent system if a single agent and a workflow will do. Less moving parts, better outcomes.
An agent makes decisions and takes actions over multiple steps. It plans, calls tools, observes results, and decides what to do next. The simplest version is a single LLM with tool-calling in a loop. The most complex is a multi-agent system where specialized agents collaborate, hand off work, and recover from failures. We build both — and we know which one your problem actually needs.
We pick by use case. LangGraph for explicit state machines, human-in-the-loop, and durable workflows. CrewAI for fast prototyping of role-based crews. AutoGen for research-style multi-agent conversation. OpenAI Agents SDK and Anthropic’s computer-use SDK for vendor-native deployments. Pydantic AI when type safety matters more than abstractions. Often we end up writing the orchestration in plain Python because the framework adds more friction than value.
Step budgets, tool budgets, cost budgets, and recursion limits — all enforced. We add planning steps that decompose work upfront, observation summarization to keep context bounded, and a watchdog that breaks the loop if the same tool is called with the same args. Every agent has a hard kill switch and structured logging of every step.
That’s usually a combination of three things: tool descriptions that confuse the model, missing fallback paths, and no eval suite. We rewrite tool schemas with examples, add retry-with-feedback loops where the agent sees its own error and corrects, and build a test suite of scenarios with expected outcomes. Reliability is engineered, not hoped for.
Yes. We build agents that drive Playwright / Browser Use for web automation, execute code in sandboxed Daytona / E2B / Modal environments, use Anthropic’s Computer Use API, and integrate with computer-control APIs like Hyperbrowser. Always sandboxed, always with audit logs, and always with a human-approval gate for irreversible actions.
Three patterns. (1) Direct API tools — we wrap your REST or GraphQL endpoints as agent tools with strict input schemas. (2) MCP (Model Context Protocol) servers when you want one tool surface across multiple agents and clients. (3) Workflow handoffs to existing systems via webhooks, queues, or RPA when the system has no API. We pick whatever ships fastest with the right safety properties.
Free 5-day PoC of one agent end-to-end. Production agent build: $20K–$100K depending on tool count, eval rigour, and human-in-the-loop requirements. Multi-agent system: $40K–$200K. Dedicated agent engineer: $22–$36/hr. Most engagements start with the PoC and convert to a fixed-scope build once the architecture is validated.
Tell us the workflow and the tools. We'll ship a traced, evaluated PoC by Friday — so you can see what the agent actually does before you commit.