Can you build AI document processing?

Yes. A common first agent reads incoming documents — invoices, contracts, applications, forms — extracts the fields you need with schema-validated output, checks them against your rules, files the result in your system and sends low-confidence cases to a person. Accuracy is measured on a sample of your real documents before go-live.

Do you also work as AI agent consultants?

Yes, through the AI Discovery Sprint: two weeks to map the workflow, check your data, design the agent and its guardrails, and give you a fixed price for the build. You can take that plan to any team, but most clients have us build it.

What is the difference between an AI agent and a chatbot?

A chatbot answers. An agent decides on and takes actions — calling your APIs, updating records, sending messages — usually across several steps. That ability is what makes agents useful and also why they need permissions, approval steps and evaluation.

Do you build with LangGraph, or something else?

LangGraph is our default for multi-step, stateful agents because it gives durable checkpoints and explicit control flow. For simpler tool-calling features we often use the Vercel AI SDK or a provider SDK directly. We choose per project and explain why.

Can the agent work with our existing software?

Yes — that is the usual case. If your system has an API we wrap it as tools; if it does not, we can build the API first. We also build MCP servers so your platform works with Claude, ChatGPT and other MCP clients.

How do you stop an agent from doing something wrong?

Scoped permissions inherited from a real user, validation in your API rather than in the prompt, human approval on irreversible steps, idempotent actions, and an evaluation suite that runs before every change. We also start in shadow mode, where the agent proposes and a person acts.

Which model do you use?

Whichever passes your evaluation set at an acceptable cost and latency — often Claude, GPT or Gemini, sometimes an open-weight model. We keep the model swappable.

AI agent development

Custom AI agents that do real work inside your systems

We design and build AI agents that read, decide and act across your software — CRM, billing, scheduling, document stores, internal tools — with the guardrails a business process needs: scoped permissions, approval steps, evaluation and logs you can audit.

Discuss your agent See related work

Why most agent demos never reach production

Getting an LLM to call a tool in a notebook takes an afternoon. Getting an agent to run a business process every day takes engineering: the agent needs access to the right systems and nothing more, it has to recover when an API times out, it must not repeat an action that already happened, and someone has to be able to see why it did what it did.

That is the work we do. We treat an agent as a piece of production software — with typed tools, state that survives a crash, tests, and a rollout plan — rather than a prompt with a few functions attached.

What we build

Workflow agents

Agents that own a defined process end to end — triaging inbound requests, preparing invoices, reconciling records, chasing follow-ups — and hand off to a person at the steps you choose.

Assistants inside your product

Agents your customers use: they can search, summarise and take actions in your SaaS on the user's behalf, with the user's own permissions.

Internal operations agents

Agents connected to the tools your team already runs on, so staff can ask for work to be done instead of clicking through five admin screens.

Multi-agent systems

When a single agent gets unwieldy, specialised agents with their own tools and prompts, coordinated by a router or supervisor — used only when it measurably beats one agent.

MCP servers for your platform

A Model Context Protocol server that lets Claude, ChatGPT and other MCP clients use your system safely. See our MCP server development service.

Agent hardening for existing prototypes

You already have an agent that mostly works. We add evals, retries, idempotency, permissions and observability so it can be trusted with real traffic.

The engineering that makes agents safe to run

These are the parts that separate an agent you demo from an agent you deploy.

Tools are your API, not a second copy of it

Agent tools call the same validated endpoints your application uses. Business rules live in one place, and the agent cannot do anything your API would reject.

Permissions inherited from a person

Each run carries the identity and scopes of the user or service account it acts for. "The AI" never holds a master key.

Durable state and idempotency

Long-running workflows checkpoint their state, so a crash resumes instead of restarting — and an action like "send invoice" cannot fire twice.

Human approval where it matters

Irreversible or expensive steps pause for approval in the tool your team already uses. Everything else runs on its own.

Evaluation before and after launch

A test set of real cases, scored automatically (including LLM-as-judge rubrics checked against human labels), run on every prompt or model change.

Tracing and cost control

Every step, tool call and token is traced, so you can answer "why did it do that?" and "what does a run cost?" without guessing.

Where agents are earning their keep

Back-office operations

Creating and sending invoices, logging time, scheduling, contract updates and follow-up emails from a plain-language request — the pattern behind our MCP case study.

Inbound triage

Reading support tickets, form submissions or emails, classifying them, pulling context from your systems and drafting the response or routing to the right person.

AI document processing

Extracting fields from contracts, applications or invoices, checking them against rules, and filing the result where it belongs, with low-confidence cases sent for review.

Customer-facing product features

An assistant in your SaaS that can actually change things — update settings, generate reports, configure records — rather than only answer questions.

Agents we have shipped

Our founder also builds LangGraph-based multi-agent systems in production on an AI EdTech platform used across 20+ US schools — durable, checkpointed workflows with per-agent tool sets and evaluation.

AI integration · MCP

Connecting an operations platform to Claude with a remote MCP server

A remote Model Context Protocol server that lets the consultancy's staff run their admin platform by asking Claude — invoicing, scheduling, follow-ups, time entries and contracts — with the same permissions they have in the app.

Read the case study AI application · Structured LLM output

AI-generated assessments and executive reports for a leadership consultancy

A team-assessment app where admins generate assessment templates from a topic with an LLM, participants answer through share links, and the system writes executive-summary reports.

Read the case study

How an agent project runs

1
Pick one workflow and measure it
We start with a single process, write down what "done correctly" means, and collect 30–100 real examples. If we cannot define success, we say so before you spend money.
2
Prototype against real data
A thin working agent on your actual systems, in a sandbox, scored against the example set. This is where we learn whether the model is the bottleneck or the data is.
3
Engineer for production
Permissions, approvals, retries, durable state, tracing and a proper interface for the people who use it.
4
Roll out gradually
Shadow mode first (agent proposes, a person acts), then supervised autonomy, then autonomy on the cases the evals show it handles reliably.
5
Operate and improve
We monitor failure cases, add them to the test set and improve prompts, tools or models — with the eval suite catching regressions.

Technology we use

Agent frameworks

LangGraph
LangChain
Vercel AI SDK
Model Context Protocol

Models

Anthropic Claude
OpenAI GPT
Google Gemini
Open-weight models via Groq

Evaluation & tracing

Langfuse
promptfoo
LLM-as-judge rubrics
OpenTelemetry

Backend

Python / FastAPI
Node.js / TypeScript
PostgreSQL
Redis
Queues & schedulers

Integration and deployment considerations

Where it runs

Inside your existing backend or as a separate service on your own cloud account. Your data does not need to pass through our infrastructure.

Data handling

We use providers' zero-retention or enterprise endpoints where required, redact what the model does not need, and log prompts and outputs only where you are allowed to keep them.

Model choice and lock-in

We keep the model behind an interface and evaluate more than one. Switching providers should be a config change plus an eval run, not a rewrite.

Cost per run

We measure tokens and tool calls per run during the prototype, so you know the operating cost before you commit to rollout.

Who this is for

Companies with a repetitive, rules-heavy process that already runs through software you control.
SaaS teams that want an assistant in their product that can take actions, not just chat.
Teams with an agent prototype that works in demos but is not trusted with production traffic.
Operations leaders who want automation that is auditable, not a black box.

What drives the cost

We quote after a short scoping call, because the spread between "agent that drafts emails" and "agent that moves money" is large. A focused first workflow is usually a few weeks of work; we prefer to prove one workflow before planning the next.

How many systems the agent must read from and write to
Whether actions are reversible, and how much approval tooling is needed
Quality of the data and APIs it relies on
Accuracy bar and the size of the evaluation set
Volume, latency and model cost targets
Security, compliance and data-residency requirements

Related services

MCP Server Development AI Integration Services RAG Development AI Chatbot Development

AI Agent Development: common questions

Tell us which workflow you want an agent to own

Tell us what you are building or what needs fixing. You will hear back from an engineer — not a sales rep — within one business day, usually with questions or a rough approach.

Your enquiry goes straight to our engineering team — the people who will scope and build your project, not a sales desk.

sales@dablewdevs.com Book a free 30-min call