AI Agents & Automation
Agents that reliably take action inside real workflows. Not impressive demos that break in production.
Most AI "agents" are chatbots wearing a trench coat. They respond to prompts but don't actually do anything. The ones we build read emails, call APIs, write files, query databases, and loop until the task is done.
What We Build
Task-Focused Agents
Every agent we ship is scoped to one job: process these invoices, monitor this queue, respond to these support tickets, enrich these records. Narrow scope means clear success criteria, measurable outcomes, and no risk of the agent touching things it shouldn't.
We don't build general-purpose AI assistants. We build agents that own a specific workflow from start to finish.
Tool-Using Agents
An agent without tools is just a chatbot. We build agents that call APIs, query databases, read and write files, send notifications, and trigger downstream workflows - with explicit permission boundaries on every action.
Common integrations: internal databases, Slack, Linear, Salesforce, HubSpot, Google Workspace, Notion, Jira, custom REST and GraphQL APIs, file systems and document stores.
ReAct Pattern Agents
We implement agents using the ReAct (Reason + Act) loop: the agent thinks through what it needs, takes an action, observes the result, then decides what to do next. This continues until the task is done or a human checkpoint triggers.
This is different from a single-shot prompt. The agent can recover from partial failures, handle unexpected data formats, and route edge cases to humans instead of silently producing wrong output.
Multi-Agent Systems
For complex workflows, we build systems where multiple agents divide work: one gathers context, another processes it, another takes action, another validates. Each agent has a defined role and output contract. Coordination happens via message queues or shared state, not by cramming everything into one context window.
How We Make Agents Reliable
Evals and Regression Tests
Every agent ships with an eval suite - test cases with expected outputs that run before every deployment. When the model, prompt, or data format changes, we know immediately what regressed.
This is the difference between an agent that worked once and an agent that works reliably across thousands of runs. Most shops skip this step. We don't.
Guardrails
Agents that write data need safety rails:
- Read vs write permission separation - agents that need to read don't get write access
- Dry-run mode for destructive operations before they touch real data
- Human-in-the-loop checkpoints for high-risk actions
- Output validation before downstream systems receive agent results
Observability
Production agents need visibility. We instrument every agent with input/output logging, latency and cost tracking per task, failure alerting with enough context to debug what went wrong, and drift detection when output quality degrades over time.
Use Cases We Build For
Document Processing - Ingest PDFs and emails, extract structured data, validate against business rules, route to the right system.
Support Triage - Read incoming requests, classify by type and urgency, draft responses, escalate what genuinely needs human judgment.
Research and Monitoring - Watch feeds, APIs, and databases; summarize what changed; surface what matters; discard noise.
Data Enrichment - Take a list of records, look up missing fields from external sources, normalize formats, write clean data back.
Workflow Automation - Trigger chains of actions based on events: when a deal closes, create the project, send the welcome email, assign onboarding tasks.
How We Work
- Scope definition - We define exactly what the agent does and what it doesn't. Ambiguous scope is the primary reason agents fail.
- Happy path first - Ship a working version against the common case before handling edge cases.
- Eval suite before edge cases - Write tests for the happy path so refactoring can't break it silently.
- Guardrails and monitoring - Wire up observability before the agent touches production data.
- Edge case expansion - Broaden scope incrementally, always with tests first.
- Handoff - You receive the agent, eval suite, runbook, and monitoring setup.