State
Runs need accurate state
A software run must expose scope, repository state, progress, and remaining uncertainty.
DeepBrainz LabsAgentFoundry Research · reviewed agent work
AgentFoundry Research studies how AI-assisted software work becomes measurable, reviewable, and safe to deploy.
This Labs page is the technical counterpart to the AgentFoundry product landing. It studies scoped runs, repository state, tool boundaries, evaluation loops, cost visibility, review records, and the evidence needed before agentic software work should be trusted.
UseOpen AgentFoundryOpen the product or path tied to the job you need done.InspectBack to Labs researchReview the workflow, model detail, or evidence before deciding.DecideOpen AgentFoundryChoose the action that creates a real next step.
Runs
Research object
Evidence
Trust object
AgentFoundry
Product link
Research problem
AI-assisted software work is only useful when the work can be checked.
The Labs story is not that agents simply write code. It is that long-horizon software systems need state, boundaries, checks, review records, cost visibility, and a clear handoff into human judgment.
Checks
Outputs need validation
Tests, review records, and traceable evidence are treated as first-class research objects.
Governance
Human review stays central
The research agenda studies where approval, rejection, revision, and deployment judgment belong.
Execution unit
Run
The page treats an agent run as the object to plan, observe, evaluate, and review.
Review unit
Record
Review reports, cost records, tests, and changed work become inspectable output.
Stack link
R1 + Lexopedia
Model behavior and workspace background feed the execution research layer.
Execution research stack
AgentFoundry Research explains what has to be true before agentic software work is credible.
The page maps the research from scoped intent through tool-mediated work, checks, evidence, and human review.
Public surface
DeepBrainz Labs
Product, research, and evidence paths stay easy to choose without turning the page into an architecture map.
01
Scope and approval
Define what the agent is allowed to do, where human approval is required, and what must stay out of scope.
02
State and tools
Track repository state, tool calls, run progress, and failure handling as inspectable system behavior.
03
Evaluation and tests
Use tests, review signals, and quality checks to decide whether a result is acceptable.
04
Review records
Produce concise material that a human can use to approve, revise, or reject the work.
Research loop
AgentFoundry Research turns agent runs into evidence loops.
The process is simple: define the work, watch what happens, check the result, and keep a record a reviewer can inspect.
Define
Run brief
Intent, repository state, constraints, and approvals are named before execution.
Observe
Visible progress
Tool use, status, cost, and intermediate state stay available for review.
Validate
Checks and tests
The system measures whether the output behaves as expected.
Decide
Human judgment
Evidence supports the final decision instead of hiding it behind automation.
Research-to-product path
The page shows how Labs findings become AgentFoundry product quality.
A technical reader can move from research questions to practical execution requirements.
Question
What can the agent safely do?
Scope, permissions, and human approval rules define the run.
Observe
What happened during execution?
State, tool calls, changes, and costs need to be visible.
Check
What evidence supports the result?
Tests, review notes, and changed-work records support judgment.
Review
What should a human decide?
The final product value is better review, not hidden automation.
Run reliability
The run is the central research object.
AgentFoundry Research studies how a software run is prepared, constrained, observed, checked, priced, and reviewed. That makes the work legible enough to improve rather than mysterious automation.
Scoped intent and human approval rules.
Visible repository and task state.
Tool-use and status traces.
Cost and review records.
Evaluation
Quality has to be measured where the work happens.
The research agenda asks how tests, review records, and human feedback should be attached to real software work. Evaluation is strongest when it tests the same files, logs, reports, and evidence that a reviewer actually sees.
Tests and quality checks.
Review reports and evidence summaries.
Changed-work records.
Approval and revision paths.
Stack relationship
AgentFoundry Research depends on the rest of DeepBrainz.
R1 improves the agent behavior available to the run. Lexopedia prepares background and technical intent. Labs studies the evidence loop that makes the execution layer credible.
R1 supplies agentic model behavior.
Lexopedia shapes research and background.
AgentFoundry carries the execution path.
Labs checks the resulting evidence.
Explore next
Move between the product, the model line, and the broader research agenda.
AgentFoundry Research sits between R1 model behavior and the AgentFoundry product for reviewed software work.
Next step
Use AgentFoundry Research to understand the evidence layer behind reviewed AI-assisted software work.
The research route makes clear that reliable agent work needs scope, visible state, checks, cost records, review evidence, and human judgment.