AI agents that do real work inside the systems you already run
An AI agent is software that uses a language model to carry out multi-step work: read an inbound request, look up records in your ERP or CRM, decide what applies, draft the response, and update the system of record. Done well, it removes the glue work between your systems — the triage, re-keying, and copy-paste that skilled people get stuck doing.
Willowark builds agents narrow and testable. Every action an agent can take is a typed tool with a defined contract, writes go through human review gates until the error rate earns autonomy, and every step is logged. We use retrieval to give agents your business context rather than fine-tuning, because retrieval is auditable, updatable, and cheaper to keep correct.
AI & Intelligent AutomationHow the work gets done
The same way every time: scope, build, hand over.
Under the hood this is engineering, not prompting. We define tool interfaces with JSON Schema validation, wrap the model in a state machine that handles retries, timeouts, and partial failures, and make side effects idempotent so a retried step never double-writes an order. Before an agent touches production, it runs against a suite of recorded real cases and gets scored — no eval suite, no deployment.
In production the agent runs off a queue, traces every step with cost per run, and escalates anything below its confidence threshold to a person with full context attached. Success is measured the way you would measure a new hire: share of cases handled end to end, error rate against the human baseline, and cycle time from request to resolution.
Scoping starts with the boundary, not the model. We sit with the people who do the work today and list the systems the agent will read from, the systems it may write to, and the decisions that must stay with a person. From that list we pick the narrowest slice still worth automating, usually one request type through one workflow, and write down what a correct outcome looks like. The main trade-off is autonomy against blast radius: a read-only agent that drafts for approval ships quickly, while an agent that writes to the ERP needs stronger validation, tighter permissions, and a longer supervised ramp before it earns that trust.
What usually goes wrong is not the model but the edges: a tool that returns an unexpected shape, an upstream API that times out mid-run, a case type nobody mentioned in discovery. We design against those by validating every tool response, making each step resumable from its last checkpoint, and treating unknown cases as escalations rather than guesses. At handover, your team gets the repository, the eval suite, the runbook for common alerts, and a walkthrough of how to add a tool or adjust a threshold. Ongoing maintenance is mostly re-running evals when a model version changes and reviewing escalations for patterns worth automating next.
Scope it in writing
What we agree before work starts
- Agent design document covering tool contracts, permissions, and escalation rules
- Production agent service integrated with your systems via API and queue
Build with checkpoints
Working results, not slide decks
- Evaluation suite built from recorded real cases, with pass thresholds
- Human review workflow for approving agent actions during ramp-up
Hand over something you own
Documentation, source, and training
- Audit logging with per-run cost and trace dashboards
- Operations runbook covering alerts, replaying a failed run, and adjusting confidence thresholds
Sound familiar?
Where ai agents earns its keep.
A support inbox where an agent triages requests and drafts replies for human approval
An agent watching stock levels across ERP and supplier portals, drafting purchase orders
A quoting desk where an agent assembles draft estimates for an engineer to sign off
A dispatcher's backlog cleared by an agent reconciling technician calendars against open work orders
Ask about AI Agents
Describe the problem. Get a straight answer.
One line is enough. An engineer replies within a business day.
Related work
AI operating software for training operations
Components:
- Schedulers (training ops): The people running the training operation.
- Operating software (AI assistance): The operating software, with AI assistance built in.
- Cloud services (distributed): Distributed cloud services behind the application.
- Training game (learning): The training game the software connects to.
Connections:
- Schedulers to Operating software
- Operating software to Cloud services
- Operating software to Training game
A global automotive manufacturer's training operation · Automotive
AI operating software for training operations
AI operating software for the manufacturer's training schedulers — removing manual scheduling labor and logistics tracking, saving hundreds of hours per year — plus a training video game built to help trainers perform better. Used across the manufacturer's training organization.
Read the case study →Common questions
Asked before every ai agents project.
How do you stop an agent from doing something wrong in production?
Three layers. Every action is a constrained tool call validated against a schema, so the agent cannot act outside its contract. Writes start behind a human approval gate until eval scores and live error rates justify autonomy. And every step is logged and replayable.
Do we need our own model, or fine-tuning?
Almost never to start. Commercial models with retrieval over your data outperform fine-tuned models for most business tasks and are far easier to update when processes change. We design the integration so the model is swappable as pricing and quality shift.
What does an agent cost to run?
Model usage is metered, so cost scales with volume — most business agents run for cents per case, and we instrument cost per run from day one. Build cost depends on how many systems the agent touches, scoped in a short discovery before quoting.
How long does it take to get an agent into production?
It depends on how many systems the agent touches and how clean their APIs are. A single-workflow agent that reads from one system and drafts for approval is typically a matter of weeks; one that writes to several systems takes longer, mostly for integration and the supervised ramp. Discovery usually takes a week or so and produces a scoped estimate before we commit to a build.
Can our own team maintain the agent after you hand it over?
That is the goal, and we build for it. The agent is ordinary application code in a repository you own, with tools defined in plain schemas, prompts versioned alongside them, and an eval suite that tells you whether a change helped or hurt. A developer comfortable with APIs can add a tool or adjust an escalation rule without us. Where you would rather not staff it, we offer ongoing support, but nothing in the design depends on it.
Where this sits
AI Agents, inside a ai & intelligent automation system.
The lit component is the part of the system this service delivers; the rest is what it has to work with.
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Inbound documents and messages are ingested, an agent reasons with company knowledge and acts through the systems of record, and a person reviews the cases that need judgment.
Components:
- Inbound (email, PDFs, forms): The unstructured work arriving every day.
- Ingestion (extract, classify): Turns documents into structured fields with confidence scores.
- Agent (reasons, uses tools): A model with tools: it looks things up, decides, and acts — within limits you set.
- Knowledge (your docs): Company procedures and history, retrieved on demand.
- Systems of record (ERP, CRM): Where the work actually lands.
- Reviewer (exceptions): The person who sees what the agent was unsure about.
Connections:
- Inbound to Ingestion over email
- Ingestion to Agent over events
- Agent to Knowledge over REST, both directions
- Agent to Systems of record over REST
- Agent to Reviewer over handoff
Strategy. Software. Systems.
Have a system that should exist?
Tell us what your operation is doing manually, what isn't connected, or what you're trying to build. We'll tell you plainly whether and how we can help.