Intelligence, with responsibility.

AI Agents.

Agents that own bounded tasks, use tools and know when to hand control back.

We design agentic systems around real jobs, explicit tool access, controlled state and evaluation, so autonomy is useful rather than theatrical.

Start an agent project See what we build
A clear objective.A deliberate limit.A human way back.
Inside the boundary
01

GOAL

The job, success condition and boundaries of responsibility.

Illustrative agent study. Not a live agent or client system.

Beyond the conversation

When a prompt is not enough to complete the job.

A model can answer a question without being able to own a workflow. Agentic systems become valuable when they can observe state, use approved tools, make bounded decisions and recover or escalate when the next step is uncertain.

  1. 01

    A recurring knowledge task requires several steps and multiple tools.

  2. 02

    A team needs an assistant that can act, not only draft an answer.

  3. 03

    Work moves across APIs, records, queues or business systems.

  4. 04

    A workflow needs machine speed but still requires human approval at defined points.

  5. 05

    A single prompt has grown into fragile chains of manual follow-up.

  6. 06

    An AI feature needs explicit state, retries, stopping conditions and operational visibility.

A job worth delegating

Not autonomy for its own sake.
Something useful to do.

01

Tool-using task agents

Agents that can call approved services and complete a bounded sequence of actions.

02

Research and synthesis agents

Systems that gather, compare and structure information before presenting a usable result.

03

Operations copilots

Assistants embedded around team workflows where context and actions matter more than generic conversation.

04

Supervisor and worker patterns

Coordinated agent structures when one workflow needs distinct specialist roles.

05

Human-in-the-loop agents

Autonomous steps combined with explicit review, approval or escalation boundaries.

06

Event-driven agents

Agents triggered by operational events, queues or system state rather than only a chat message.

The agreed outcome

Bounded autonomy with a job to finish.

Useful agents need a defined objective, access to the right tools, controlled memory and clear conditions for retry, escalation and stopping.

The Intelligence Yard standard
Responsibility / Defined
  1. 01Defined autonomy boundary
  2. 02Approved tool access
  3. 03State and memory strategy
  4. 04Human handoff paths
  5. 05Evaluation scenarios
  6. 06Operational traces

Final deliverables follow the agreed project scope.

From objective to operation

Give the agent a job.
Then test its judgment.

  1. 01

    DEFINE THE JOB

    Describe the exact outcome the agent owns and what remains outside its authority.

  2. 02

    MAP THE TOOLS

    Identify the APIs, data and actions the agent is allowed to use.

  3. 03

    BOUND AUTONOMY

    Define permissions, stopping rules, retries, escalation and human checkpoints.

  4. 04

    BUILD THE LOOP

    Implement planning, tool use, state and recovery around the real workflow.

  5. 05

    EVALUATE

    Test success, failure, ambiguity and unsafe or incomplete paths against realistic scenarios.

  6. 06

    OPERATE

    Add tracing, review and iteration so the agent can be improved from observed behavior.

Our wider AI methodology
Technology fit

The work chooses
the tools.

A relevant toolkit, not a compulsory stack. The final choices depend on the task, existing systems and operating constraints.

  • Python
  • TypeScript
  • LangGraph
  • FastAPI
  • PostgreSQL
  • Redis
Explore the technology landscape
Where we draw the line
Autonomy without boundaries is not intelligence. It is operational risk.

Junkyard Mind / Intelligence Yard

Before we begin

Worth asking.
Clearly answered.

01What makes an AI agent different from a chatbot?

An agent is designed to pursue a bounded goal, maintain workflow state and use approved tools or actions, rather than only respond conversationally.

02Can agents call our existing systems?

Yes, when suitable APIs or interfaces exist and the actions can be constrained safely.

03Can a human approve important steps?

Yes. Human review and approval gates can be part of the workflow wherever judgment or risk requires them.

04Do agents need memory?

Sometimes. Memory should be added only where the workflow benefits from persistent context, and its scope should be explicit.

05How do you test agent behavior?

Evaluation should cover realistic success cases, ambiguity, tool failure, incomplete information and escalation behavior.

06Can multiple agents work together?

Yes, when separate roles genuinely simplify the workflow. Multi-agent design is not automatically better than one well-bounded agent.

HAVE A JOB AN AGENT SHOULD OWN?

A real job.
A better way.

Bring us the workflow, the tools it touches and the decisions that still require a human.

Let’s define
the work