Every component, together.
Instructions, tools, context, memory, orchestration, policy, and models all contribute to one versioned agent.
Improve the
whole agent.
Continuously.
Improve your agent's instructions, tools, context, and memory. Train your own model for your workflow. Compare complete versions in simulation, deploy the best, and keep improving from real-world experience.
Agent improvement
Complete versions
Compare changes| Component | |||
|---|---|---|---|
| Agent | Base | Base | Base |
| Harness | Base | Updated | Updated |
| Model | Base | Base | Trained |
Task completion accuracySame evaluation tasks
Compare the complete agent.
Run each configuration under the same conditions. Compare outcomes, reliability, cost, and latency before selecting a version for production.
THE SENTINIUM PLATFORM
Continuous improvement infrastructure for agentic systems.
Train your own models. Improve your agents, harnesses, and tools. Evaluate complete versions in simulation and use production feedback to guide the next improvement.
Train weights. Own the checkpoint.
Curate trajectories for supervised training, or train through interaction and reward feedback in RL environments. Training changes model weights and produces a checkpoint you own.
Output: owned model checkpointIMPROVE THE SYSTEM
Change a component.
Evaluate the whole agent.
A retrieval change alters what the model sees. A memory change alters how it plans. A model change alters how it uses tools. Their value emerges in the system they form together.
01
Make a focused change
Refine one component at a time and capture the full configuration as a new agent version.
02
Observe the interactions
Run that complete version in simulation. Inspect execution traces to see how the change affects decisions, tool calls, state, and task outcomes.
03
Carry improvement forward
Keep effective changes, compare the next candidate, and deploy the version that performs best for your objective.
THE COMPLETE AGENT
Every component is part of the improvement loop.
Refine the agent and harness, or introduce a newly trained model. Evaluate each change in a complete version to see how the parts work together.
ONE VERSION CONTAINS THE COMPLETE AGENT Select a component to explore
01 / SIMULATIONTasks. Tools.
Tasks. Tools.
Changing state.
Run complete versions in repeatable environments. Capture execution traces and evaluate task outcomes.
AGENT VERSION Harness + model
02 / SELECTIONSelect the best
Select the best
complete version.
Compare outcomes, reliability, cost, and latency. Deploy the selected configuration.
Instructions
Refine the objective, instructions, and stopping conditions. Save the change in a new agent version and evaluate it with the existing tools, memory, and model.
↳ Simulation and production traces guide the next component change, training task, and evaluation scenario.
SIMULATION ↔ PRODUCTION
The improvement loop continues in production.
Use production traces to identify failures, build new simulation scenarios, and guide the next improvement. Refine the agent and harness, train your model, and compare complete versions before the next deployment.
Select a stage to explore
01 / 06Version
Change a part. Version the whole.
Improve an agent or harness component, or introduce a separately trained model checkpoint. Capture the configuration together as a complete version.
Agent + harness + model
FOLLOW A VERSION THROUGH THE LOOP
From the next change to the next deployment.
See how a component change becomes a complete agent version, how simulation guides selection, and how real-world interactions inform the next iteration.
CONTINUOUS AGENT IMPROVEMENT SIMULATION ↔ PRODUCTION
v1 → v2
Change one component. Create a complete version.
Choose an agent or harness change, such as revised instructions, retrieval, tools, or memory. A model-training experiment produces a new checkpoint instead. Record either change in a complete version so its effect can be compared.
Agent & harness
Change instructions, tools, context, memory, or orchestration.
Model training
Use curated trajectories or RL environments with reward feedback to train an owned checkpoint.
Version
Record the harness configuration and model checkpoint together.
SUCCESSIVE COMPLETE VERSIONS
Find the configuration that performs best.
Build on what works. Compare each candidate against earlier versions under the same conditions, with task outcomes, reliability, cost, and latency evaluated together.
| Version | Next change | Complete configuration | Evaluate in simulation |
|---|---|---|---|
| v1 | Starting configuration | Instructions, tools, context, memory, policy, and model | Establish the baseline |
| v2 | Refine context retrieval | v1 + new retrieval; model unchanged | Does better evidence improve the whole task? |
| v3 | Refine orchestration | v2 + revised orchestration; model unchanged | How do the harness changes work together? |
| v4 | Train a model checkpoint | v3 harness unchanged + checkpoint from curated trajectories or RL training | Does the trained model improve the complete agent on separate evaluation tasks? |
| v5 | Refine memory retrieval | v4 + revised memory; trained model unchanged | Does the harness improvement hold across longer tasks? |
SELECT → DEPLOY
vnext ↻The best-performing version moves to production.
Choose by task outcomes, reliability, cost, and latency. Carry its complete configuration into the real world, then use production traces and outcomes to guide the next iteration.
Evaluate againstTask outcomesReliabilityCostLatency
CONTINUOUS IMPROVEMENT
A better agent is an ongoing process.
Every version, compared.
Run the complete configuration in simulation. Observe how component changes interact and which version best meets your objective.
Every deployment, a new baseline.
Promote the selected version to production. Use execution traces and outcomes to identify failures, expand simulation scenarios, and guide the next improvement.
IMPROVE THE COMPLETE AGENT
Make every version of your agent count.
Bring your agent and the outcomes that matter. Build a continuous path from component changes to better production behavior.