Agents that finish real work.
Goal-directed AI that executes multi-step work: it dials leads, reads and files documents, answers customers, and hands off to a person when it should. Reliable enough to put your name on it.
Content reviewed
The demo is easy. The 10,000th run is the product.
Anyone can wire a model to a tool and record a demo. The difference between that and a production agent is everything that happens when reality pushes back: ambiguous inputs, failed API calls, an angry customer, a compliance question. That is the part we engineer.
Our agents ship with guardrails, evaluation suites and full audit trails. Every action is logged, every edge case has a fallback, and a human can take over mid-task. More than a hundred thousand live calls have run through this setup. The failure modes we found along the way are now scripted test cases, and every change has to pass them.
Agents with a job description.
We scope every agent to one defined role with a measurable output, then build only the capabilities that role needs.
Voice agents
Outbound and inbound calling that qualifies, books and routes through natural conversation. A human can take over the call live at any point.
Workflow agents
Multi-step processes executed end-to-end: document intake, claims triage, order processing, follow-up sequences.
Retrieval-grounded assistants
Support and knowledge agents that answer from your own data and cite the source document for every answer.
Integration services
Agents wired into your CRM, ERP and ticketing through APIs and MCP. They update records themselves and log every change.
Industry-specific copilots
Domain agents trained on your playbooks and constraints, from aviation compliance to food-service ops.
Evals & guardrails
Behavioural test suites, output constraints and monitoring. All three keep an agent on-script while models and prompts change underneath it.
Four stages to autonomy.
We expand what an agent may do only as it proves itself, the same way you would with a new hire.
01Define the job
One role, clear inputs, measurable output. We write the agent’s job description and success metric before any code.
02Human in the loop
First deployments run supervised: the agent proposes, a human approves. You read the real transcripts and decide what it may do on its own.
03Measure & harden
Eval suites run on every change. We chase down the 2% of runs that fail and engineer them out.
04Scale autonomy
Approved action classes go autonomous; sensitive ones keep approval gates. Full audit trail at every stage.
Your product could be the next case on this page.
Start with a free 4-hour workshopWe have shipped this before.
A multi-tenant agent platform we built and operate — auto-dialling, qualifying and routing leads through natural conversation.
Lead Track AI
Multi-tenant SaaS that automates lead engagement with AI-powered voice agents — auto-dialling prospects, qualifying through conversation, and routing high-intent leads to sales with full context.
What we build with.
Orchestration, telephony and evaluation tools, picked because they hold up when calls run all day.
Disciplines we combine.
Disciplines most often combined with AI agents — same architecture, same engineers, no integration tax.
Straight answers.
The things buyers of AI agents ask us most.
Put it in a brief. A senior engineer reads it and replies within one business day.
Q.01How do you stop an agent from going off-script?
Constrained outputs, allow-listed actions, and behavioural eval suites that run on every prompt or model change. Sensitive actions sit behind approval gates, and every run is logged so you can audit exactly what the agent did and why.
Q.02Can agents work inside our existing systems?
Yes — that is most of the value. We integrate via your APIs and the Model Context Protocol, so agents read and write to your CRM, ERP or ticketing system under the same permissions model as a human operator.
Q.03Voice agents sound robotic. Do yours?
Modern speech models hold natural, interruptible conversation with sub-second latency. Ours have completed 100K+ live calls; we will play you unscripted recordings from that traffic before you commit.
Q.04What happens when the agent gets stuck?
It escalates. Every agent we ship has explicit failure behaviour: hand off to a human with full context, queue for review, or roll back the task. Failing silently is not on the list.
Q.05How long does an agent take to build?
A single-purpose internal agent: 4–6 weeks. A customer-facing agent with multiple tools, approval flows, and observability: 10–16 weeks. We always start with a paid 1-week scoping spike.
Q.06Single agent or multi-agent — when do you split it up?
Default to a single agent with a clear tool set; multi-agent adds coordination overhead and failure surface. We split into planner/executor/critic roles only when a single context window can't hold the task, when sub-tasks need genuinely different tools or models, or when one role's output must be independently reviewed before another acts.
Q.07How do you stop an agent from looping forever or burning the budget?
Hard step limits, a per-task token and dollar budget enforced at the orchestration layer, and loop detection that catches when the agent repeats the same tool call with the same arguments. When a budget trips, the agent escalates to a human rather than failing silently — we use LangGraph or Temporal so the run is checkpointed and resumable.
Q.08How do you test an agent before it touches production systems?
We run it against sandboxed or mocked tools with a suite of scripted scenarios covering happy paths and known failure modes, then replay real (anonymised) traffic in shadow mode. The behavioural eval suite runs on every deploy and blocks merges that regress on safety or completion-rate benchmarks.
Have a workflow an agent
should own?
Describe the job — qualifying, triaging, answering, processing. We will reply within one business day with an honest read on whether an agent can actually do it.