How do you stop an agent doing something expensive or wrong?

Step limits, cost ceilings, an allowed-action list and a human approval queue for consequential actions. Before go-live the agent runs in shadow mode and we compare its proposed actions to what people did.

Can agents work with our existing tools?

Yes. We expose your systems through MCP servers with typed schemas and permissions, which also means the same tools can serve future agents without being rebuilt.

Do you build voice agents?

Yes. One production deployment handles 80%+ of a tier-1 queue with a sub-second loop and a warm transfer when confidence drops. Details are on the case studies page.

Agentic AI and Automation

Agents that complete real tasks, with the guardrails that let them run unattended

Planner, typed tools over MCP, a policy gate with step and cost limits, and a human approval step where the stakes are high. Orchestrated on LangGraph, traced end to end, and wired into n8n for the workflows around it.

Book a free scoping call See the work

900+: Automated triggers a day, revenue layer
61%: Support queries resolved without escalation
80%+: Tier-1 calls handled by a voice agent
38%: Less incident time, DevOps agents

What we build

Agentic AI and Automation, delivered as a working production system

Scoping, architecture, build, evaluation, deployment, monitoring and handover under 1 engagement, delivered by the engineer who designs it.

Task agents on your systems

Agents that draft, review, qualify, enrich and reconcile, connected to your CRM, databases, ticketing and APIs through typed tool contracts. Full audit trail on every action.

MCP tool layer

Model Context Protocol servers that expose your internal systems to any agent framework with schemas, permissions and rate limits, so the same tools serve several agents safely.

Orchestration and state

LangGraph state machines with checkpointing and replay, so a failed run resumes from the last good step and a wrong decision can be traced to its inputs.

Approval gates and policy

Step limits, cost ceilings, allowed-action lists and a human-in-the-loop queue for anything that moves money, contacts a customer or changes a record of consequence.

n8n workflow automation

Self-hosted n8n inside your infrastructure, 400+ integrations, AI decision logic in every workflow, no per-operation pricing. Lead routing, enrichment, reporting and triage that run around the clock.

Voice agents

Streaming speech recognition, grounded retrieval and a resolution policy that decides what the agent may close alone. Sub-second loop, warm transfer with transcript when confidence drops.

Computer-use agents for systems with no API

Agents that operate a vendor portal or legacy screen through the browser like a trained clerk: read the task, navigate, fill forms, check the result, with approval on sensitive steps and screenshots of every step.

Intelligent process automation

Document processing, compliance checks, data extraction and multi-system orchestration where rule-based RPA breaks. AI reasoning and vision on the variable, judgment-heavy steps, deterministic workflow around them, no disruption to live operations.

Architecture

The agent loop we ship by default

A trigger starts a planner that calls tools through MCP. Every proposed action passes a policy gate: step count, spend, allowed actions. High-stakes actions wait in a human approval queue. Every step lands in Langfuse with inputs, outputs and cost, so the run can be replayed and audited.

Where these projects usually fail

The 4 mistakes we get called in to fix, and what we do differently

Open-ended loops with no step limit

An agent that can retry forever will. Every agent we ship has a step budget, a cost ceiling and a defined failure action.

Tools without contracts

Free-text tool calls break on the first malformed argument. Typed schemas, validation and idempotent writes are the baseline.

No shadow run before go-live

Agents run in shadow mode against real traffic first. We compare proposed actions to what humans did, and only then let them act.

Nobody can explain what the agent did

Tracing is not optional. Every run is stored with its reasoning, tool calls and cost so a disputed outcome can be reconstructed.

Proof

Delivered systems in this practice area

Every figure comes from a system running in production for a real client.

Multinational enterpriseMulti-region · AWS

Search and automation across 5 million files that cannot leave their regions

1. Problem. 5 million+ files across regional data centers, S3, document systems, databases and application servers: contracts, financial statements, engineering reports, spreadsheets and email. The usual fix is 1 central index, and regional data rules ruled it out. So every cross-region question, and every workflow touching more than 1 system, depended on someone stitching answers together by hand.

2. Solution. The data stays in its region and the question travels to it. Retrieval runs locally and returns only what the asking user is already allowed to open. 7 LangGraph agents split the work, hand off to each other over A2A and reach each system through MCP servers with scoped permissions. Anything that changes a record waits for human approval, and n8n keeps ingestion, approvals and notifications moving around the clock.

LangGraphA2AMCPn8nOpenSearchLangSmith

Client name withheld under NDA. Ongoing 12-month program; figures are from the agreed evaluation set and the delivered milestones.

3. Impact

5M+: Files searchable across regions, permissions intact
97.86%: Task-success benchmark target on the agreed evaluation set
7: Specialised agents coordinating over A2A
12 months: Ongoing program: 6 delivered, 6 more contracted

Enterprise SaaSUSA and UK

An autonomous revenue layer that closed the gaps no rep could watch

1. Problem. 14 disconnected sales tools, no consistent lead qualification and shaky CRM data. Revenue was slipping through gaps that no single person could track across multiple regions at once.

2. Solution. A round-the-clock revenue layer on n8n and LangGraph. Real-time lead qualification, automatic CRM enrichment, personalized outreach across email and LinkedIn, predictive deal health scoring, and live pipeline reports delivered to leadership with no sales ops in the loop.

n8nLangGraphGPT-5.6HubSpot API

3. Impact

48%: Reduction in manual sales ops workload
1.8x: Increase in qualified lead throughput
$340K: Pipeline generated in the first 6 months
900+: Automated workflow triggers daily

All 13 case studies

Stack

What we build on, and why it is current

Model names and tooling are kept up to date with what we are shipping this quarter. The selection logic is simple: the option that passes your benchmark at the lowest cost wins, and that is tested rather than assumed.

Frameworks

LangGraphClaude Agent SDKOpenAI Agents SDKGoogle ADKCrewAI

Protocols

MCPA2Afunction callingstructured outputswebhooks

Automation

n8nMakeZapierPower AutomatePlaywright computer-useUiPath

Voice

OpenAI RealtimeDeepgramElevenLabsTwilioLiveKit

Observability

LangfuseLangSmithArize PhoenixOpenTelemetry

Questions

Before you book a call

Generative AI DevelopmentLLM applications, RAG systems and copilots with a real backend AI/ML EngineeringModel development, fine-tuning, forecasting and computer vision Cloud and MLOpsAWS, Azure and GCP infrastructure, monitoring and CI/CD

A workflow follows a fixed path and is the right tool when the steps are known. An agent decides the next step from context and is worth the extra engineering only when the path varies. Most engagements use both: n8n for the deterministic parts, an agent for the judgment calls.

Your AI system should work, not just demo well

Book a free 30-minute scoping call. You leave it with a recommended approach, a realistic timeline and a cost range, and you talk to the engineer who would design the system.

Book a free scoping call Send project details

Written scoping summaryyours after the call

1Recommended approachRAG with hybrid retrieval, or classical ML, or plain software. Including the option of not building.
2What the system ownsThe steps it resolves alone, and the steps that stay with a person.
3First metric and baselineThe number it has to beat, measured on your own data before any build.
4Timeline and cost rangePhased, with the first scored result inside 14 days.

Written by the engineer who would build itFree, no obligation, yours to take to any vendor

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