Context & retrieval
Bring the right documents, state, and task instructions into each step.
UNDER THE HOOD / TECHNICAL CAPABILITIES
Great models are
only the beginning.
The system around a model—and the environment it learns from—can make all the difference. We engineer both.
01 / HARNESS FRONTIER INTELLIGENCE
A powerful model.
A purpose-built system.
A harness is the software around a model that supplies context, connects tools, manages execution, and checks its work. It turns model capability into a repeatable business workflow.
We’ve seen remarkable results from custom harnesses built around frontier models. Our work focuses on the details that make those models effective in practice: the right information, well-designed tools, and feedback that helps them recover and finish the job.
REASONING ENGINE
Frontier model
Plans · reasons · selects actions
Tools & orchestration
Connect APIs and software with clear interfaces, scoped permissions, and execution budgets.
State & recovery
Persist progress, handle tool failures, and resume long-running workflows.
Verification & oversight
Check outputs, record execution traces, and route consequential decisions for review.
A harness operates at inference time. It can improve the overall system without changing the model’s weights. We compare complete workflows on task success, failure modes, latency, and cost—not just the quality of a single response.
02 / BUILD THE PLACE WHERE LEARNING HAPPENS
What is an
RL environment?
A reinforcement learning environment is a controlled setting where a model attempts a task, takes actions, and receives feedback. It defines what the model can observe, what it can do, and how success is measured.
INSIDE A TRAINING ENVIRONMENTILLUSTRATIVE SCENARIOS
INDUSTRIAL MAINTENANCE / 01
Task in context.
Diagnose a fault from sensor readings and a service history.
Example designs, not client results. A training algorithm uses scored attempts to update model parameters; a single interaction does not automatically retrain the model.
The environment makes practice possible.
We build realistic tasks, tool interfaces, simulators or sandboxes, resettable starting states, and reward functions. Together, these produce repeatable attempts that a training pipeline can learn from.
The training loop turns practice into skill.
The model generates attempts. Verifiers or calibrated evaluators score outcomes. An RL algorithm uses those signals to update the model, then we test the resulting behavior on cases excluded from training.
Reward design is central. A model can learn to exploit an imperfect scoring rule. We test for shortcuts, use independent checks, and involve domain experts when correctness cannot be verified automatically.
03 / MAKE RARE EXPERTISE REPEATABLE
Your domain doesn’t
need to be mainstream.
Specialized terminology, uncommon tools, and operational edge cases may be poorly represented in broad training data. A domain-specific environment can create focused practice around precisely those gaps.
Capture expertise.
Translate expert procedures, representative examples, and business constraints into tasks and evaluation criteria.
Create variation.
Generate and validate diverse scenarios, including unusual cases and failures, rather than repeating a small set of answers.
Shape behavior.
Reward successful decisions and tool use across increasingly demanding tasks. Use supervised examples when foundational behavior needs a starting point.
Test transfer.
Hold out cases and scenario families. Check whether the model handles unfamiliar work, not just the tasks it practiced.
Focused practice needs trustworthy substance. RL does not manufacture missing domain knowledge. Strong base capabilities, validated examples, expert feedback, and access to reference material may all be necessary. Retrieval and supervised fine-tuning can complement reinforcement learning.
04 / CHOOSE THE RIGHT LEVER
One problem.
Several ways forward.
We choose the approach around your task, evidence, and operating constraints. Harness engineering and model specialization can work together.
| Approach | What changes | Where it helps |
|---|---|---|
| Custom harness | The execution system around the model | Multi-step workflows, tool use, recovery, and verification. |
| Retrieval / RAG | The reference information available at runtime | Private, specialized, or frequently changing knowledge. |
| Supervised fine-tuning | Model weights, using demonstrated examples | Consistent formats, domain patterns, and demonstrated behavior. |
| Reinforcement learning | Model weights, using rewards across attempts | Sequential decisions and specialized tasks with meaningful, reliable feedback. |
05 / ENGINEERING YOU CAN EVALUATE
Built around evidence.
Delivered for operation.
We scope each engagement around a measurable outcome and the artifacts your team needs to maintain it.
What we can deliver
- Custom model harnesses and production tool integrations
- Resettable RL environments, task sets, and reward logic
- Training pipelines and specialized model checkpoints
- Evaluation suites, trace analysis, and deployment documentation
How we judge the result
- Held-out task performance against a relevant baseline
- Error patterns, edge cases, and human review requirements
- End-to-end latency and cost per successful task
- Training and maintenance costs alongside inference savings
The goal is specialized performance that can exceed a frontier baseline at a fraction of the inference cost. Whether that is achievable depends on the task, model, training signal, and deployment economics. We establish the evidence before making the claim.
FURTHER READING
For background on the underlying ideas: Anthropic on effective agents ↗ and the DeepSeek-R1 research paper ↗. These explain technical foundations; they are not Agentis Labs case studies.
LET’S GET SPECIFIC
What should your
AI be exceptional at?
Bring a workflow, a hard edge case, or a niche domain. We’ll help map the right system and a way to measure it.
Talk through your use case