AI Agents and Agentic Automation
Agents that complete work rather than describe it, with scoped permissions, evaluation on whole trajectories and an audit trail. Thirteen engagements, each with its own scope, price and output.
Permissions make this different.
An agent is software that decides what to do next and then does it, inside systems that hold your customers, your money and your record of the truth. That is a meaningful thing to hand to a probabilistic component, and it is why almost all of the engineering in this pillar goes into the parts that are not the model: scoped tools, bounded loops, reversible writes, trajectory evaluation and a tested way to stop.
This is our deepest capability and the one we publish most about. Every agent we build runs read-only against live traffic before it is allowed to write anything, and every engagement hands over the evaluation practice as well as the code.
What we build
Each is a standalone engagement with its own scope, price and output. Most clients use two or three in sequence.
Custom AI Agent Development
An agent that completes work in your systems, with scoped tools, trajectory evaluation and a circuit breaker.
Read more →Multi-Agent System Orchestration
Several agents coordinated with explicit hand-off contracts and contained failure, only where one agent genuinely will not do.
Read more →Agentic Workflow Automation
End-to-end processes automated with models used only where judgement is required, and exceptions handled by design.
Read more →MCP Server Development and Integration
Your internal systems exposed as safe, scoped, audited tools that any compliant agent or assistant can use.
Read more →Tool and Function-Calling Integration
Tools designed for a model to call correctly: strict schemas, idempotent writes and errors that teach.
Read more →Agentic RAG
Retrieval that plans, decomposes and verifies for questions single-shot RAG cannot answer, with the cost stated.
Read more →Voice AI Agents
Voice agents built as real-time systems, with a latency budget, natural turn-taking and a fast route to a human.
Read more →AI SDR and Sales Agents
Sales agents that research before they write, respect deliverability and compliance, and hand over cleanly.
Read more →AI Customer Support Agents
Support agents that resolve tickets end to end, with real actions, grounded answers and honest escalation.
Read more →AI Research and Analyst Agents
Research agents that work approved sources, cite every claim and separate finding from inference.
Read more →Browser and Computer-Use Automation Agents
Agents that operate a browser where no API exists, with isolated credentials, injection defence and honest limits.
Read more →AgentOps: Monitoring, Evaluation and Guardrails
The operations layer for production agents: trajectory tracing, evaluation, spend ceilings and a tested kill switch.
Read more →Agent Framework Migration
Move between agent frameworks with behaviour parity proven by evaluation, and a tool layer that survives the next move.
Read more →How they fit together
You do not need all twelve. Most programmes follow one of these paths depending on where the uncertainty sits.
One task, one agent
Start with custom AI agent development on a task with a checkable outcome, with the tool layer designed properly first. Add AgentOps at the same time rather than after the first incident.
A process rather than a task
Agentic workflow automation, which keeps deterministic steps deterministic and applies judgement only to the exception queue where the cost actually sits.
Customer-facing
Support agents for resolution rather than deflection, voice agents where the channel is the phone, and sales agents where deliverability and brand safety decide whether it works at all.
Already running agents
AgentOps for control and measurement, tool redesign where reliability is the complaint, and framework migration where the stack is the constraint.
FAQ
Marked up with FAQPage schema so these answers can surface in search results and inside AI assistant responses.
What is the difference between an AI agent, a copilot and a chatbot?
A chatbot answers questions. A copilot prepares work for a person to approve inside the tool they already use. An agent completes work itself through tools, which is why it needs scoped permissions, reversible writes and an audit trail. Many organisations are better served by a copilot first, and we will say so if that is our read of the task.
Is it safe to give an AI agent write access to production systems?
For tasks whose outcome can be checked, with scoped tools, idempotent writes, bounded loops, a tested circuit breaker and a read-only shadow period behind it, yes. Without those controls, no. Most agent incidents come from missing controls rather than from a model being wrong.
Where should we start with AI agents?
With one task that has a checkable outcome, in systems that have APIs, and a shadow period before write access. If the use case is not yet chosen, AI use case discovery comes first; if feasibility is the question, a four to six week proof of concept is the smaller bet.
Do we need a multi-agent system?
Less often than the market suggests. Multiple agents earn their complexity when the work spans different permission domains, when one agent is degrading under too many tools, when independent verification adds value, or when parallelism shortens a long process. Otherwise a single well-scoped agent wins on latency, cost and debuggability.
Who owns the code, tools and evaluation sets?
You do, in full, on final payment: agent code, tool implementations, prompts, evaluation and trajectory sets, dashboards and infrastructure as code. Nothing about running the agent afterwards depends on us remaining involved.
Start with a conversation.
Thirty minutes, no charge, no deck. Tell us what you are trying to build with language models and we will tell you which of these engagements fits, or whether none of them do.