Agentic Systems & AI Employees
Agentic systems have moved from research demos to production. The question is no longer whether agents can do useful work. It is how to deploy them with enough access to be productive, enough guardrails to be safe, and enough evaluation to be trusted. We design and ship both sides.
Typical turnaround when the target workflow and access model are already defined.
Across engagements to date, from 15-engineer scaleups to 5,000+ employee global enterprises.
Agents ship with an evaluation harness, monitoring, and escalation paths, not just a prompt.
Strategy & architecture
- Agentic system strategy and sequencing roadmap
- Workflow mapping to identify high-leverage agent targets
- Agent architecture design (single-agent, multi-agent, handoffs)
- Tool and permission model for agent access
- Integration patterns with existing systems and data
Coding agents
- Coding-agent rollout for engineering teams (Claude Code, Cursor, Codex, Cline)
- AI PR-review configuration with human escalation
- Test-generation and evaluation agents
- Repository-specific prompt and skill libraries
- Reviewer handoff patterns for agent-authored changes
AI employees
- AI employee scoping (support, research, operations, internal ops)
- Onboarding frameworks for AI employees into team workflows
- Escalation paths and human-in-the-loop design
- Knowledge access via RAG pipelines over your sources
- Role-based metrics and quality review cycles
Evaluation & monitoring
- Evaluation harness with offline and online test suites
- Quality monitoring (drift, regression, cost, latency)
- Incident detection and rollback patterns
- Agent performance reviews as an operational practice
Diagnostic
Review a candidate agent workflow. We return a shortlist of the most deployable agents and the highest-risk ones, plus an architecture sketch for the top candidate.
Hands-on Demo
Walk through a production agent setup with real code, evaluation harness, and monitoring. Shows the architecture and trade-offs for your use case.
Sprint
Hands-on design and deployment of one agent or one agentic workflow, with evaluation harness and monitoring wired in from day one.
Embedded Retainer
A dedicated senior consultant embeds with your team under your engineering leadership. Incident response, new agent design, model upgrades, quarterly reviews. Monthly time-and-materials.
Best fit
- Engineering teams rolling out coding agents across dev workflows
- Product teams deploying conversational AI or AI employees into production
- Platform teams building agent infrastructure for other internal teams
- Organisations that need agents evaluated before they ship, not after
Not a fit
- Pure research or proof-of-concept work with no deployment target
- Organisations unwilling to invest in evaluation and monitoring
- Use cases where a deterministic workflow would be strictly better
HoverBot
AI-native chatbot platform
The client needed a production-grade AI platform that could support configurable chatbots, knowledge-grounded responses, and safe enterprise-friendly workflows.
Read case studyDomain-specific conversational AILabCaddy
Scientific product platform
The client needed a more intelligent way for users to discover science-related products and interact with product information through conversation, not just keyword filtering.
Read case studyAI workflow transformationEngineering AI Adoption
B2B SaaS · Series B · 15-person engineering team · APAC
A software company wanted to adopt AI across engineering in a practical way, but needed the right workflows, training, governance, and rollout model to make it useful and compliant.
Read case study01Coding agents vs. AI employees: what's the difference?
Coding agents sit inside developer tooling (IDE, CI, PR flow) and accelerate engineering work: code authoring, review, test generation, refactoring. AI employees are longer-running agents that participate in team workflows end-to-end, for example handling tier-one support tickets, doing research, or running internal operations. Architecturally they share a lot; operationally they're quite different.
02How do you decide where to deploy agents first?
We look for workflows with high repetition, clear inputs and outputs, bounded scope, and tolerable failure modes. Coding agents usually pass all four criteria inside the PR cycle. AI employees usually pass inside a narrow workflow (e.g., intake triage) before you expand scope.
03What does an evaluation harness look like?
A repeatable test suite that measures agent quality on representative tasks, usually a mix of offline benchmarks (deterministic tests) and online evaluations (human or LLM-judged). We wire evaluation into the deployment pipeline so you know when a model upgrade, prompt change, or tool change breaks the agent, before it hits production.
04Do you build agents from scratch or use existing platforms?
Both. For coding agents, we usually use established platforms (Claude Code, Cursor, Copilot, Codex) and configure them deeply. For AI employees, we typically build on a mix of LLM providers, orchestration frameworks (LangGraph, custom), and your own tool integrations. Platform choice is an engagement-specific decision driven by your stack and constraints.