Agentic AI solutions
An assistant answers a question. An agent is handed a goal, works out the steps, uses your systems to carry them out and reports back. That is a bigger promise, and it needs a tighter engineering discipline to keep.
We design, build and run AI agents that take real work off your team — triaging requests, reconciling records, chasing exceptions and updating systems of record — with scoped permissions, human approval where it matters and a full audit trail of every step.
Agents that do the work, not just describe it
Process Agents
Agents that own a defined back-office process end to end — order exceptions, invoice matching, claim follow-up — and hand a person only the cases that genuinely need judgement.
Tool & API Integration
Your CRM, ERP, ticketing, email and data platforms exposed to the agent as well-described, permission-scoped tools, so it acts through the same systems your team already uses.
Multi-Agent Orchestration
Specialist agents for research, planning, execution and review coordinated by an orchestrator, where one agent trying to do everything would be slower and less reliable.
Human-in-the-Loop Workflows
Approval steps placed where the risk is — payments, customer-facing messages, record deletions — so an agent can prepare the action and a person signs it off in one click.
Agent Evaluation & Testing
Scenario suites that score whole task runs, not single answers: did the agent finish, take the right path, stay within its permissions and cost what it should.
Agent Operations
Monitoring, tracing, cost tracking and alerting for agents in production, with every step, tool call and decision recorded for review and audit.
How an agent goes from idea to production
Pick the process
Choose a repetitive, rules-heavy task with a measurable baseline — volume, handling time, error rate — and clear boundaries.
Map the actions
List every system the agent must read from or write to, and decide which actions it may take alone and which need approval.
Build the tools
Wrap those systems as narrow, well-documented tools with their own permissions, rate limits and input validation.
Test on real cases
Run the agent against historical cases in a sandbox, scoring completion, accuracy and the path it took to get there.
Shadow, then hand over
Run alongside the team first, then let the agent act on its own as confidence and the evaluation scores earn it.
What keeps an autonomous system accountable
An agent that can act can also act wrongly. These controls ship with the first release, not in a later phase.
- Least-privilege tool access, scoped per agent and per task
- Approval gates on irreversible or customer-facing actions
- Spending, step and rate limits enforced on every run
- Prompt-injection testing against the content agents read
- Full trace of reasoning, tool calls and results for audit
- Automatic escalation to a person when confidence is low
- Dry-run and sandbox modes for every new capability
- A kill switch that returns the work to the existing process
What teams ask before letting an agent act
How is agentic AI different from a chatbot or copilot?
A chatbot or copilot responds to a person and leaves the action to them. An agent is given a goal and carries out the steps itself — looking things up, updating records, sending messages — within the permissions it has been granted.
Which processes are a good first candidate?
High-volume work with clear rules and a few judgement calls: exception handling, data reconciliation, request triage, follow-ups. Avoid starting with anything where a single mistake is expensive and hard to reverse.
Will the agent make changes without anyone checking?
Only where you decide it should. Every action is classified up front — automatic, needs approval, or never allowed — and that policy is enforced in code rather than left to the model.
Which models and frameworks do you use?
Whichever suits the task, data-residency rules and budget — commercial and open-weight models alike. The tools, policies and evaluation suite are built as your own assets, so the model underneath can be swapped later.
Where we have done this before
- View detailsAI agents & customer self-servicePackaging8 min read
One AI service agent for orders, invoices, pricing and support
A global leader in consumer packaging, industrial products, protective packaging and packaging supply chain services
B2B buyers had to call or email customer service for order status, invoice copies, pricing and stock. We built an Agentforce Service Agent for the customer’s website that verifies each buyer and answers from live Salesforce and ERP data — ten subagents in one conversation, with a human handoff when it is needed.
- Orders, tracking, invoices, pricing and stock answered in chat
- Live ERP data, not a cache
- Verified identity and handoffs with a visible wait time
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