Long Horizon AI

Give your agents a goal.
Give your team
room to grow.

Bring AI teammates into the flow of your organization. TensorOps builds agents that carry complex tasks forward for hours or days, learning from experience and keeping your team in the loop.

Build your AI team Watch a workday
Delegate in Slack. Follow the board.
Make room for the work only you can do.
THE AGENT WORKDAYSlack → AgentScrum → ReviewIllustrative workflow
Slack
01 / 04
A message becomes a mission.

Give your agents the outcome and the boundaries. The work starts where your team already talks.

Explore AgentScrum

Illustration: Alex delegates a password reset feature to Developer and QA in Slack. Agents plan and complete their work on AgentScrum, then return a pull request and test report to Alex for review.

A new rhythm of teamwork

Work with your agents
like you work with your team.

Set an outcome, agree on boundaries and let your agent take ownership. Get updates in Slack, check tasks on AgentScrum and step in for the decisions that benefit from your judgment.

01

Share the outcome

A Slack message gives your agent the context, scope and definition of done.

02

Let the work unfold

Agents plan, use tools and coordinate handoffs, with progress visible on the board.

03

Review the result

Return to a clear deliverable, supporting evidence and the next decision.

AI operations teams

Your everyday work.
A team that follows through.

Connect the systems your organization runs on. An agent harness brings together MCP tools, a current knowledge base and a learning memory, so recurring tasks can move from request to checked outcome.

TensorOpsYOUR SYSTEMS. A TEAM THAT KEEPS WORK MOVING.
THE AI OPERATIONS ARCHITECTURE

Connect the knowledge.
Coordinate the work.

Give your agents the context, tools and boundaries to handle recurring operations.

An AI operations team connected through a governed agent harnessSlack and events feed an agent harness. A knowledge base and scoped memory provide context. The harness uses an MCP tool gateway to access internal systems and propose approved actions. Independent evaluation feeds reviewed experience back into memory.01 / RECEIVE02 / UNDERSTAND03 / COORDINATE04 / CONNECT05 / DELIVERVerify outcomes → evaluate strategies → approve memory updatesSlack & ticketsEmployee requestsEvents & schedulesRecurring workKnowledge baseRunbooks & policiesLearning memoryScoped, reviewed experienceAgent harnessPlan · run · checkpointGoals · budgets · escalationMCP gatewayScoped tool accessInternal systemsCRM · IT · dataChecked actionsEvidence & approvals
  1. 01
    Requests & events

    Slack messages, tickets and scheduled work start a scoped task.

  2. 02
    Knowledge & experience

    Retrieve current runbooks, policies and approved memories.

  3. 03
    Agent harness

    Plan, run, checkpoint and escalate within a clear scope and budget.

  4. 04
    MCP tool gateway

    Connect to internal systems through authenticated, permissioned tools.

  5. 05
    Checked outcomes

    Verify results and request approval for consequential actions.

  6. 06
    Evaluated learning

    Review task traces and promote effective strategies into scoped memory.

Permissioned toolsIndependent outcome checksHuman approval for consequential actions
One workflow to start with

A scheduled reconciliation checks your CRM against billing records, investigates mismatches with the relevant runbook, and brings proposed corrections to an owner for approval. The verified resolution becomes a candidate for future learning.

Give your agents an efficient inference foundation
Autonomy with accountability

Risks and opportunities
in Long Horizon AI.

The opportunity is to delegate meaningful work and step away from the screen. That freedom works best when the system can verify outcomes, recognize its limits and bring the right decisions back to your team.

The opportunity: a team that keeps improving.

Capture task traces and verified outcomes. Preserve useful insights in scoped memory, and use evaluated trajectories to improve skills, tool selection and model policies where training is supported.

  • Reusable routes and proven tool sequences
  • Rewards grounded in independently checked outcomes
  • Versioned improvements tested before rollout

The risk: persistence in the wrong direction.

An agent can repeat a flawed action, optimize a misleading score or carry outdated instructions into a new situation. We pair clear authority with isolated execution, independent evaluation and a reliable way to ask for help.

  • Tool permissions, network boundaries and budgets
  • Monitoring for reward hacking and goal drift
  • Escalation, rollback and reviewable audit trails
CODING SCENARIO

A green test needs trustworthy evidence.

An agent might change a test to improve its score. Keep acceptance checks independent, review the diff and require evidence that the original requirement is met.

OPERATIONS SCENARIO

A useful action needs a clear boundary.

An agent might repeat an outdated runbook or act on instructions embedded in a ticket. Version policies, scope tool access and pause consequential actions for approval.

The self-learning architecture

A continuous learning loop.
A clear path to production.

Run the work with a tested policy. Learn from the evidence. Promote improvements through evaluation and approval.

Governance across every stepPermissions · Isolation · Budgets · Monitoring · Human approvals
  1. 01

    Define the goal

    Slack brief, success criteria, deadline and permissions

  2. 02

    Plan & coordinate

    Task breakdown, owners and progress in AgentScrum

  3. 03

    Run & checkpoint

    Scoped tools, efficient inference and resumable work

  4. 04

    Verify & deliver

    Independent checks, evidence and human review

Verified outcomes + task traces
01 / CAPTURE

Curated experience

Effective routes, tool calls and scoped insights

02 / IMPROVE

Memory + RL training

Skill updates and candidate policies, where supported

03 / VALIDATE

Independent evaluation

Held-out tasks, alignment checks and human approval

Approved, versioned improvements return to the next run.Monitor rollout · Roll back when needed
Reference architecture. Memory updates and model training are separate processes; production changes pass through evaluation and approval.
Where long-horizon work creates value

Build teams that move
your organization forward.

Start with a clear outcome, a reviewable deliverable and an owner. Expand the agent’s responsibility as the evidence earns your confidence.

01

Self-running R&D teams

Keep coding agents moving through a scoped backlog. Product leaders focus on the problems worth solving; engineers own architecture, review and quality. Agents implement, test and prepare the next change, helping your team ship faster.

Build a feature overnight. Return to a tested pull request, QA evidence and a concise review brief.
Your team owns
Product direction, architecture and release approval
Your agents carry forward
Implementation, regression testing and review preparation
The opportunity
Shorter delivery cycles with engineering quality gates
02

AI operations teams

Give recurring work a team of its own. Agents use MCP tools, your knowledge base and reviewed experience to investigate requests, reconcile systems and coordinate the actions that used to require repeated human attention.

Reconcile customer records across systems, investigate exceptions and prepare approved corrections.
Your team owns
Policies, exceptions and consequential approvals
Your agents carry forward
Context gathering, routine checks and tool-driven handoffs
The opportunity
More operations completed with fewer manual handoffs
03

Research that keeps exploring

Turn a research question into a sequence of bounded experiments. Agents propose changes, run evaluations and preserve what works. Your researchers choose the questions, validate the evidence and direct the next round.

Explore retrieval settings or agent strategies overnight, then compare the strongest candidates on held-out tasks.
Your team owns
Research questions, evaluation design and interpretation
Your agents carry forward
Hypotheses, experiment execution and evidence logging
The opportunity
More tested ideas for every research cycle
Inspired by Karpathy’s autoresearch

Ask. Experiment. Evaluate. Learn.

Autoresearch gives an agent a bounded training experiment, a fixed time budget and a measurable result. The agent tests changes, retains improvements and repeats. We adapt that pattern to your research questions and evaluation criteria.

  1. HypothesizeChoose a promising change
  2. ExperimentRun inside a fixed budget
  3. EvaluateMeasure against independent criteria
  4. LearnKeep useful evidence; propose the next step

Reinforcement learning extends this approach where training is supported: use verified outcomes as rewards, explore candidate strategies and evaluate improved policies before rollout. The experiment loop itself is distinct from training the agent’s model.

Explore the autoresearch project
What changes in your organization

More capacity.
A new way to organize the work.

As long-horizon agents become part of the team, people spend more time defining valuable problems and evaluating results. The workflow shifts toward clear ownership, visible progress and evidence-based decisions.

ROLES

People set the direction.

Product leaders shape outcomes. Engineers and domain experts define standards, review the work and own important decisions. Agents take on the execution between those checkpoints.

WORKFLOWS

Delegate outcomes, review evidence.

Teams assign scoped work in Slack, follow progress on a shared board and review complete deliverables. Clear escalation paths make it easier to step away while work continues.

OUTCOMES

Measure the work that matters.

Track delivery cycle time, accepted changes, successful resolutions and validated experiments alongside cost and quality. Use those results to decide where to expand autonomy.

Build the next chapter together

Big goals.
A team that keeps going.

Bring us a workflow you’d love to delegate. We’ll shape the first agent, its boundaries and a practical path to production.

Let’s build your AI team
Long Horizon AI | TensorOps