AI agents for workflows that need to take action, not just answer questions.
We design agents around approved tools, scoped data access, human review points and measurable task outcomes — then run them with the evaluation and monitoring a production system needs.
- Task and permission design
- Tool and system connectors
- Orchestration and guardrails
- Evaluation suite
- Review console and audit trail
- Monitoring and runbook
Where this helps
What we deliver
How it works
- 01
Task discovery
We sit with the people who do the work today, pick one bounded task with a clear success measure, and record a baseline.
- 02
Design the boundaries
Tools, permissions, approval points and failure behaviour are agreed and written down before any build starts.
- 03
Build and connect
We implement the agent and its connectors in short iterations, testing against real (or realistic) cases each week.
- 04
Evaluate
The agent runs against the evaluation set and in shadow mode alongside the team until results meet the agreed bar.
- 05
Release in stages
Start with human approval on every action, then relax approvals only where the evidence supports it.
- 06
Operate and improve
We monitor, review failures with your team and expand the agent's scope one task at a time.
Design decisions we make with you
Autonomy level
Which actions the agent can take on its own, which need approval, and which it may never take. This is a business decision, not a model setting.
Identity and access
The agent gets its own credentials with least-privilege scopes, so every action is attributable and revocable.
Model choice
We pick models per step — a smaller, cheaper model for routing, a stronger one for reasoning — and keep the choice swappable.
Cost and latency
Step limits, caching and model routing keep per-task cost predictable; we report it alongside quality.
Ownership
You own the code, prompts, evaluation sets and logs. The system runs in your environment or ours, by agreement.
Applications
Related capabilities
- Customer Support AgentsResolve routine customer inquiries from your policies and live account data, taking approved actions and handing everything else to your team.
- Workflow AutomationMove multi-step back-office work between systems automatically, with approvals where they matter and a record of every step.
- Document IntelligenceClassify, extract, check and route incoming documents, with people reviewing the exceptions.
- AI Workflow AutomationAdd AI judgment — reading, classifying, deciding within limits — to multi-step processes, with rules where rules work and people where judgment matters.
- AI IntegrationConnect AI models and assistants to your CRM, ERP, databases, knowledge stores and APIs — with identity, logging and fallbacks that production systems need.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
- Generative AI & LLM DevelopmentApplications built on large language models that are grounded in your data, tested against real cases and costed before launch.
Questions buyers ask
A chatbot holds a conversation and answers questions. An agent can also take actions in other systems — create a ticket, update a record, issue a refund — within the limits you set. Many projects start as a chatbot and add agent actions once the answers are reliable.
Three layers: the agent only has credentials for the tools and scopes it needs; risky actions require a person to approve them; and hard limits cap steps, spend and the size of any change. Every action is logged with the reasoning behind it.
No. We need access to the systems the task touches and a set of real examples. Data gaps usually show up in the first two weeks, and we fix the ones that block the task rather than cleaning everything.
You do — code, prompts, evaluation sets and configuration. See how we work for engagement terms.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.