AI agents,
engineered as software.
Reasoning, tool use, memory, orchestration, evaluation and human-in-the-loop controls, designed against the risk of the workflow, and shipped with the observability any production system deserves.
Agents earn their place when the workflow needs planning, tool use and adaptation to a variable context. Where the work is a fixed sequence of steps, a script or an orchestration engine is cheaper, faster and more reliable, and we say so.
Frame the workflow
First question: does this actually warrant an agent, or would a script win? We answer honestly.
Triage queues with heterogenous cases, research-style tasks, conversations that branch, agents earn their place. Fixed sequences with stable schemas, agents don't.
Sometimes framing kills the agent. That's the highest-return work we could have done.
The healthiest agent estate is the smallest one that meets the outcome. We build to that, not past it.
Prototype
A rigorous prototype against real tasks and a real eval, not a demo. Topology, model and tool surface sized before anything is built to last.
Single agent, planner-executor or multi-agent chosen per use case. Tool surface typed and scoped. Trajectories logged for evaluation from day one.
Most useful production agents are single-agent with a well-designed tool surface. We default to that shape.
Productionise
Full stack, orchestration, tools, memory, guardrails, observability, human-in-the-loop, integrated with your systems.
Prompt-injection defence, tool scoping, output policy, factuality checks, layered proportional to blast radius. Every trajectory is reproducible in observability.
Deploys are gated by evals, not by opinions.
Operate
Task-level and trajectory-level evals in CI. Cost and latency instrumented per step, per model, per version.
Human review feedback flows back into evals and prompts. The system gets better over time, not just at launch.
Knowledge transfer is designed in from day one. Your team owns the agent estate on the day we scope down.
Toolkit
Framework, platform and vendor selection is driven by the shape of the work and the team who'll own it, not by whichever ecosystem is loudest this year. The list below is representative, not prescriptive.
- Models
- Anthropic · OpenAI · Hugging Face
- Orchestration
- LangGraph · LangChain · CrewAI · MCP
- Memory & retrieval
- Pinecone · Weaviate · pgvector · Redis
- Observability
- LangSmith · Langfuse · OpenTelemetry
- Serving
- Bedrock · Vertex AI · Kubernetes · Temporal
Selected work
What clients ask us first
01When is an agent the right answer?
When the workflow needs planning, tool use and adaptation to a variable context. When it's a fixed sequence with a stable schema, a script or orchestration engine wins.
02Single agent, planner-executor or multi-agent?
A working decision, not a preference. Most useful production agents are single-agent with a well-designed tool surface, we default there. Multi-agent pays off when the domain genuinely has specialist roles.
03How do you handle safety and tool-use risk?
Layered defence proportional to blast radius. Tools scoped and typed, destructive actions require confirmation, inputs and outputs pass through filters, trajectories logged, red-team pass before mainnet.
04How do you evaluate quality when trajectories vary?
Task-level evals grade outcomes. Trajectory-level evals grade the process, tool calls, budget, safety, using programmatic checks and LLM-as-judge calibrated against human graders.
Let's discuss your agent programme.
Share a brief overview of the workflow, we'll arrange an introductory conversation with the relevant practice leadership.