Agents that ship work, not just answers.
For ops-heavy teams, SaaS platforms, and enterprises drowning in repetitive multi-step tasks. Production-grade agents with tool calling, human checkpoints, and full observability — not demos that break on edge cases.
An agent isn't a chatbot with extra steps. It's a system that needs failure modes designed upfront.
Most agent demos look impressive until something unexpected happens — and something unexpected always happens. We design agents backwards from the edge cases: what can go wrong, where does it need human approval, and how do we audit every action it took after the fact.
We build tool-using agents on top of OpenAI, Anthropic, and open-source models. Every agent ships with a permission model (what tools it can call and under what conditions), a budget guardrail (max cost per run), a retry/backoff policy, and a structured log you can replay. These aren't optional — they're what separates a reliable system from a liability.
We orchestrate agents with LangGraph, AutoGen, CrewAI, and custom state machines depending on the task. We pick the framework that gives you the most debuggability — not the one with the best marketing.
What we build.
Research & synthesis agents
Agents that browse, scrape, summarize, and produce structured reports — for competitive intelligence, due diligence, content pipelines, and market research.
Ticket & issue triage agents
Read incoming tickets, classify intent, pull relevant context from your knowledge base, draft a response or resolution, and escalate ambiguous cases to your team.
Document processing agents
Extract, validate, and route structured data from invoices, contracts, forms, and medical records — with exception queues for low-confidence extractions.
Ops automation agents
Multi-step workflows that span your CRM, ERP, data warehouse, and communication tools — triggered by events, run on schedule, or invoked via API.
Code & pull request agents
Agents that read your codebase, write unit tests, fix linting issues, review PRs against your style guide, and create implementation plans from specs.
Sales & outreach agents
Research prospects, personalize outreach, sequence follow-ups, log activities to your CRM, and surface hot signals to your sales team.
Where agents deliver the most leverage.
Financial services
Transaction monitoring, alert triage, report generation, and client communication drafting.
How we build production agents.
Task audit
We map the exact workflow the agent will own — every input, every tool call, every output, every decision point. We define success metrics before writing code.
Tool & permission design
We design the tool schema, permission model, and escalation policy. We define what the agent can do autonomously vs what requires human approval.
Build & eval
We build the agent, wire the tools, and run it against a curated test suite covering happy paths, edge cases, and adversarial inputs. We measure cost and latency per run.
Shadow mode
We deploy in shadow mode alongside existing workflows — the agent acts, but a human reviews every action before it takes effect. We calibrate for two weeks before going live.
Production & tune
We go live with budget guardrails and a kill switch. We tune weekly based on failure logs and edge cases caught by the human reviewers.
Tools we use.
Engagement models.
Proof of Value
from $22k
One agent, one workflow, shadow mode for 2 weeks, production for 2 weeks.
- Single workflow automation
- Up to 8 tool integrations
- Full audit logging
- 30-day post-launch support
Production Suite
from $55k
Multi-agent system covering 3–5 workflows, with shared memory, budget controls, and a monitoring dashboard.
- 3–5 automated workflows
- Shared context & memory layer
- Cost + latency dashboards
- 90-day post-launch tuning
Enterprise Platform
custom
Platform-level agent infrastructure for teams running agents at scale across departments.
- Unlimited workflow agents
- RBAC + audit compliance
- Private deployment
- Ongoing retainer
Frequently asked.
5 questions answered. Still have one? Reach out.
In narrow, well-defined workflows with good test coverage: very reliable. In open-ended, under-specified tasks: less so. We scope every engagement around workflows where the reliability bar is achievable — and we design human checkpoints for everything else.