Service / 02 Build
Agentic AI & Multi-Agent Systems
Agents that use tools to complete multi-step work, engineered with limits, approvals and evaluation.
/ 01The business problem
Why this matters
Agent demos are easy; dependable agents are not. Real agents face ambiguous inputs, flaky APIs, prompt injection from the content they read and the temptation to keep going when they should stop.
We build agents the way we build any production system: narrow scope first, explicit permissions, tests for failure, and people in the loop where actions carry consequences. Often we recommend a simpler workflow, and we will say so.
Who it is for
- Operations teams with multi-step, judgement-heavy work
- Product teams adding agentic features to software
- Platform teams standardising agent infrastructure
/ 02Scope
What the engagement covers
- 01Task analysis and agent-versus-workflow decision
- 02Tool and connector design with permission boundaries
- 03Planning, memory and hand-off design
- 04Evaluation suites including adversarial cases
- 05Approval and escalation interfaces
- 06Tracing, cost controls and deployment
/ 03Technical approach
How we work on it
Prefer the simplest pattern
We start with a single model call or a fixed workflow and add autonomy only where the task requires it.
Constrain the action space
Tools are narrow, typed and validated. Consequential actions require confirmation.
Evaluate continuously
Scenario tests run on every change, and production traces feed new test cases.
/ 04Process
From first call to production
- 1
Consultation
A focused conversation about the problem, constraints and what success would mean.
- 2
Discovery
Evidence gathering: data samples, process walkthroughs and a measured baseline.
- 3
Proposal
Scope, approach, team, timeline, risks and a fixed or estimated price.
- 4
Delivery
Iterative sprints with demos and evaluation results at each milestone.
- 5
Handover or run
Documentation, runbooks and training, or ongoing operation by our team.
/ 05Deliverables
What you receive
- Agent architecture and threat model
- Production agent services and connectors
- Evaluation suite and quality dashboard
- Reviewer and approval interface
- Runbooks and handover documentation
Engagement options
Fixed-scope sprint
Two to six weeks with a defined deliverable and price, suitable for discovery, assessments and prototypes.
Delivery team
A cross-functional squad for a build or integration, planned in milestones with demos and evaluation results at each one.
/ 06FAQ
Common questions
When should we use multiple agents?
Rarely at first. Multi-agent designs help when sub-tasks need different tools or context, but they add coordination failures. We split only when tests show a benefit.
Which frameworks do you use?
We are framework-neutral and often write thin orchestration directly against model APIs, which keeps behaviour transparent and testable.
Related solutions
- SolutionCustomer Support & CXFaster, more consistent service: AI triage, agent assist and self-service that hands over cleanly.
- SolutionIT & EngineeringService desk automation, incident assistance and engineering knowledge, connected to your tooling.
- SolutionSales & Lead OperationsCleaner pipelines and better-prepared sellers: enrichment, routing, research and follow-up drafting.
Related product concepts
Talk to an engineer about agentic ai & multi-agent systems
Bring the problem and any constraints. You will leave the conversation with an honest view of the options, even if the answer is not us.