Execution & orchestration
Design task decomposition, tool execution, checkpoints, and stopping conditions. Use a single agent or multiple agents according to the workload.
05 / AI engineering
Agent Harness Engineering
Build the execution layer that gives agents tools, state, controls, and a dependable way to finish work.
The opportunity
Give your agents a reliable way to work.
The model is one component of an agent. The harness is the application around it: the execution loop, tools, state, context, policies, and recovery behaviour. We engineer that layer around the task so an agent can make useful progress within clear boundaries.
From scope to system
What we build with you.
A focused implementation, shaped around your systems, data, and operating requirements.
Context & state management
Manage task state, session history, retrieval, and context budgets. Decide what persists, for how long, and under whose access.
Policy & recovery
Enforce permissions, execution budgets, sandboxing, and human approvals. Handle tool failures and retries without repeating consequential actions.
Tracing & evaluation
Capture useful execution traces and build task-level evaluations for completion, correctness, cost, and failure recovery.
An example in practice
A research task with a traceable path to completion.
An illustrative agent searches approved sources, assembles evidence, drafts a structured brief, and checks the output. A reviewer approves the final deliverable before distribution.
ILLUSTRATIVE USE CASE
01Define task
02Retrieve & use tools
03Validate result
04Request approval
Define success early
Measure the work.
Improve the outcome.
We agree on relevant baselines and acceptance criteria before implementation. Depending on your scope, these may include:
- Task completion and correctness
- Recovery from tool failures
- Budget and permission adherence
- Human review effort
What is an agent harness?
It is the software that surrounds a model and manages how an agent works: tools, context, state, execution, approvals, and evaluation. It turns model outputs into a controlled application workflow.
Do we need a multi-agent system?
Not necessarily. Multiple agents can help with clearly separated tasks, but add coordination cost and failure modes. We start with the simplest architecture that meets the requirements.
Can you use our preferred agent framework?
Yes, subject to its suitability and operational constraints. We can extend an existing framework or build a focused harness, with clear interfaces and documentation for your team.
From ambition to action
Your next advantage
starts here.
Bring us your requirements. We’ll help turn them into a clear scope and a practical path forward.