Engineer the Agent System
Turn frontier models into reliable agent systems for your environment.
Engineer the context and harness around frontier models so software-development agents can work safely and consistently.
Beyond the model
The model is only one layer of the system.
Reliable agents need the right context and knowledge before they begin, tools and permissions to act, constraints that keep them within bounds, and evaluation that shows whether the work succeeded.
Together, these layers are often called the agent harness. Harness engineering is the work of making the context, tools, controls, orchestration, and feedback around the model coherent and reliable.
Whether you’re designing a new development agent or strengthening an early software-delivery pilot, we identify which layer is limiting performance and engineer the surrounding system for repeated, real-world use. Our focus is the agents and harnesses that help teams design, build, review, secure, and operate software.
Ways we can help
Make the whole agent system dependable.
We work across three connected areas. The mix depends on what the agent needs to know, what it needs to do, and how you will know it worked.
Agent context and knowledge systems
Agents cannot rely on prompt heroics or start every run cold. We design the context and durable knowledge that bring your team’s conventions, decisions, and prior work into each run, then carry useful learning forward.
Areas of expertise
- Context architecture and retrieval
- Repository-connected knowledge bases
- Conventions, decisions, and prior work captured where agents can use them
- Context selection, freshness and provenance
- Durable memory and write-back loops
- Instrumentation for context use and gaps
Agent tools and integrations
Agents become useful when they can act on real systems safely. We build tools and integrations with clear contracts, scoped access, observable failures, and human control where it matters.
Areas of expertise
- Agent harness architecture and orchestration
- MCP servers, skills, plugins, and APIs
- Tool contracts, schemas, and failure behaviour
- Authentication, scoped permissions, and short-lived credentials
- Human approval and escalation boundaries
- Sandboxing, auditability, and diagnostics
- Integration with internal systems and delivery workflows
Agent evaluation and quality engineering
Reliable systems need more than a compelling output. We define what success means, build repeatable evaluation and regression practices, and instrument quality, latency, cost, and failure patterns so the system can improve with evidence.
Areas of expertise
- Success criteria grounded in real work
- Representative tasks and evaluation datasets
- Automated regression suites and graders
- Human review and evaluator calibration
- Model, agent, and harness comparisons
- Production telemetry and continuous evaluation loops
A proven foundation
Use a proven foundation, or engineer the system your environment requires.
The CodeLantern Platform is one proven implementation of governed workflow, durable knowledge, secure integrations, and delivery analytics. When it fits, we use it to accelerate the engagement.
Organizations with specialized workflows, internal platform investments, or security and compliance requirements may need a customer-owned harness instead. We can engineer or strengthen that system using the same expertise in context architecture, tools and permissions, orchestration, evaluation, and operational feedback.
Durable project knowledge, accumulating session by session, so each run starts from what the last one learned.
Related work
Relevant Solutions
Official Partner
Get started
Show us the development-agent system you’re building.
Bring us a real repository and development-agent initiative: a new design, an early prototype, or a workflow that needs to become dependable. We’ll assess its context, harness, tools, permissions, and evaluation, then recommend whether to begin with a four-week Spark or shape a consulting engagement around the agent system.
- A conversation with a senior engineer
- A practical read on the surrounding system
- A candid recommendation on where to start