AI agents and automation that do the work and show their working
We build agents that plan, act through your systems, verify the result and report back, inside guardrails you define. And we will tell you when a plain workflow is the better answer.
An agent is a loop: perceive the situation, plan the next step, act through a tool, verify what happened, and either continue or report. The engineering is in the ring around that loop, which decides what the agent may touch, how far it may go and when it must ask.
The agent loop and its guardrails. The dashed ring is enforced in code outside the model; the model cannot talk its way past it.
Who this is for
Work that is repetitive, multi-step and verifiable
Agents earn their keep on tasks with many small decisions, several systems, and a way to check the result. These are the patterns we build most.
Support resolution agents
Look up the order, check the policy, issue the refund within limits, update the ticket, draft the reply. Human sign-off above a threshold. See customer support AI.
Back-office processing agents
Invoices, onboarding packs, compliance checks: gather documents, extract, validate against rules, chase missing items, file the result.
Engineering and data agents
Triage alerts, run diagnostics through MCP tools, propose a fix as a pull request, never merge without review. Built with Claude Code and custom tooling.
How to decide
Three levels of automation, and when each wins
Most "we need an agent" requests are better served by a cheaper level. We score the task before we pick the pattern.
Level
Control flow
Who decides the next step
Typical failure
Use when
Scripted workflow
Fixed, written in code
The developer, in advance
Breaks on an unexpected input, loudly
Rules are known and stable; inputs are structured
LLM-in-the-loop workflow
Fixed steps, a model handles the fuzzy ones
The developer; the model classifies, extracts or drafts within a step
Wrong extraction or classification, caught by validation
Steps are known but some involve language, documents or judgement
Autonomous agent
Dynamic, chosen at run time
The model, within limits and tool scopes
Wanders, loops or over-reaches; needs limits and verification
Steps vary per case, several systems are involved, success can be verified
When not to use an agent
We decline to build an agent, and suggest a workflow instead, when any of these are true:
Deterministic rules already describe the task. A model adds cost and variance, not value.
There is no reliable way to verify success. An agent that cannot check its work will confidently finish wrong.
Actions are irreversible and cannot wait for approval. Payments, deletions and legal notices need a human gate.
The path is latency-critical. Multi-step loops take seconds to minutes, not milliseconds.
The agent would need data it must not see. Fix the access model first; see prompt injection defence.
What we deliver
An agent you can audit, limit and switch off
Tool layer with least privilege
Each tool has a narrow contract, its own credentials and a scope. Read-only where possible. Exposed through APIs or MCP servers; see tool use.
Limits and approvals
Step counts, token and spend budgets, timeouts and approval thresholds, enforced in orchestration code the model cannot change.
Verification and evals
Checks after every action, and an eval suite of end-to-end scenarios scored on every change, including adversarial ones.
Audit log and observability
Every plan, call, result and approval recorded and traceable, with cost and latency per run in LLMOps monitoring.
Map the task
Steps, systems, decisions, failure cases and how success is verified. Decide the automation level.
Build the tools
Narrow, tested tool contracts first. The agent is only as safe as what it can call.
What is the difference between an AI agent and a chatbot?
A chatbot answers. An agent acts: it decides which steps to take, calls tools such as APIs, databases or MCP servers, checks the result and continues until the goal is met or a limit is hit. The loop and the tools are what make it an agent. See the glossary entry.
How do you stop an agent doing something damaging?
By design, not by hoping. Every agent gets an explicit list of allowed tools with least-privilege credentials, a step and spend limit, human approval for high-impact actions such as payments or deletions, and an audit log of every call. Untrusted content is treated as data, never as instructions.
How long does an agent project take?
A single, well-bounded workflow agent typically takes six to ten weeks to reach production, including evals and security review. Multi-agent systems or agents touching several core systems run longer. We always start with the narrowest useful loop.
Which models do you use for agents?
Claude is our usual first choice for agentic work because of its tool-use reliability and MCP support; we also build on GPT and Gemini, and use open-weight models where data must stay in-house. The decision is made against your eval set, not by preference.
Can an agent work inside Salesforce?
Yes. We build custom agents that read and write Salesforce through its APIs or an MCP server, and we implement Agentforce where it fits. The choice depends on where the workflow lives and how much control you need.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.