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Portland engineering teams are good at building product. They're not built to babysit manual ops processes that should be automated. We design and ship AI agents that replace specific repetitive workflows: ticket routing, vendor onboarding checks, internal approval chains, release note generation. Real automation, not chatbots.
Every agent we build runs on durable execution (Temporal or LangGraph), has structured observability, and is written so a three-to-five person team can own it without hiring ML platform engineers. We hand off a working system, not a prototype.
Who this is not for: teams that need customer-facing chatbots, companies that want a quick proof-of-concept without production intent, or orgs that need more than six months of ongoing development before a first production deployment.
Most agent projects fail in production because they were designed around the happy path. We design around failure modes first.
We use Temporal for long-running workflows that need durable execution and replay, and LangGraph for multi-step reasoning chains where branching logic matters. We choose the framework based on your workflow, not on what we prefer to work with. Both run on AWS, which is where most Portland infrastructure already lives.
Agents that can't call your systems are demos. We map your internal APIs, Jira, Slack, GitHub, and any third-party services the workflow touches, then wire typed tool definitions so the agent can act on real data with controlled permissions. Every tool call is logged.
Not every decision should be fully automated on day one. We build configurable review gates: the agent pauses, posts a Slack message with its proposed action, and waits for approval before proceeding. Thresholds for auto-approval can be raised over time as confidence in the agent grows.
Every agent ships with structured logging, trace IDs on every workflow run, and a dashboard showing success rate, latency, and human override rate. You can see exactly what the agent did and why. We integrate with whatever you already have: Datadog, CloudWatch, or OpenTelemetry.
PDX engineering culture leans toward event-driven architecture, strong opinions on open-source tooling, and flat team structures that make heavyweight vendor contracts a non-starter.
We deploy agents on ECS, Lambda, and SQS because that's what Portland teams already operate. No new cloud accounts, no added vendor complexity. Infrastructure as Code in Terraform, checked into your repo.
Agents that react to events are easier to test and easier to extend than polling-based designs. We wire triggers through EventBridge or SQS so your agent responds to what happens in your systems rather than running on a schedule.
A three-person ops team should be able to debug a stuck workflow at 11pm without calling us. We write runbooks, instrument alerts, and document every decision point in the agent so your people own it after launch.
Running in your AWS environment, wired to your systems, passing your staging tests.
Temporal or LangGraph diagrams for every workflow, exported as SVG and checked into your repo.
Step-by-step for every failure mode we identified during build, with escalation paths.
Pre-built Datadog or CloudWatch dashboard showing the metrics that matter for this agent.
Documentation on how to adjust approval thresholds and add new review gates without code changes.
We stay on Slack for thirty days after production launch to catch anything that only shows up at real scale.
We start with a two-day discovery session where we map your current manual workflows, identify the three or four processes where an agent would save the most time, and produce a written spec. You own that spec whether or not we build together. Scoping is a paid engagement, and the fee is credited toward the project if you proceed.
Cost depends on scope: a single-workflow agent with observability, human-in-the-loop review steps, and a documented handoff is a smaller project than a multi-workflow or multi-system build. We quote fixed scope, not hourly, so you know the number before work starts.
Eight to fourteen weeks for a focused workflow agent. Weeks one and two are discovery and architecture. Weeks three through eight are build, integration, and testing in your staging environment. Weeks nine through fourteen cover load testing, observability tuning, and a phased production rollout. Complex multi-system agents can run sixteen to twenty weeks.
We write agents your existing engineers can own. Every agent ships with a runbook, Temporal workflow diagrams, alert playbooks, and a thirty-day post-launch support window. We also offer a quarterly maintenance retainer for teams that want us to handle dependency updates and model version changes as they come.
More questions? Send us a message or read the full service overview.