Skip to content
AI Services

AI Agent Development Constrained, Auditable, Recoverable

AI agent development builds systems that plan a task, call tools, and act across your software rather than only answering questions. We design the tool boundary, approval gates, failure recovery, and audit log first, because an agent that can act is only ever as safe as the constraints around it.

  • Least-privilege tool access
  • Human approval before irreversible steps
  • Full trace on every action
Design an AI AgentAll Services

How it runs · 5 steps

  1. Map the task and its blast radius
  2. Design the tool boundary
  3. Place the approval gates
  4. Build recovery paths
  5. Trace and evaluate

Built with

  • LangGraph
  • Temporal
  • MCP
  • Claude
  • GPT
  • Llama
  • OpenTelemetry
  • Structured traces

More in AI Services

Questions first?Talk to an engineer

The framework is not the interesting question

The interesting question in agentic AI is what happens on step four of a seven-step task when an API returns something unexpected. We define which tools an agent may call, what requires human approval, how a partially completed task is rolled back or resumed, and how every action is recorded for review.

Frameworks change every few months. Those decisions do not, and they are what separates an agent you can operate from one you can only demonstrate.

The Problem

What this usually fixes

  • An agent with more access than it needs

    Capability inherited from a broad API key rather than granted deliberately, so the blast radius of a mistake is unknown.

  • Half-finished tasks

    The agent failed at step four and left the world in a state nobody designed — records created, emails sent, nothing reconciled.

  • Irreversible actions taken unattended

    Sending, paying, deleting, and publishing happen without a person seeing the reasoning first, and the first review is the incident review.

  • Success measured one response at a time

    Per-response scoring says nothing about whether a seven-step task completed correctly, which is the only thing that matters.

How We Work

The process

Each step produces something you can review — a document, an environment, or working software — rather than a percentage in a status report.

  1. Map the task and its blast radius

    What the agent is for, what it may touch, and what the worst outcome is if a step goes wrong unattended.

  2. Design the tool boundary

    Each tool explicitly defined, scoped to least privilege, and validated on input and output — capability granted, never inherited.

  3. Place the approval gates

    Irreversible actions pause for a person by default, with the agent presenting its reasoning and its evidence for the decision.

  4. Build recovery paths

    Durable execution so a partially completed task can be resumed or rolled back rather than abandoned mid-flight.

  5. Trace and evaluate

    Full traces on every run and task-level success measurement across a fixed test set, so failures attribute to a step.

What You Get

Why teams choose this

  • A known blast radius

    You can state exactly what the agent is able to do without a human seeing it first — because it was designed that way.

  • Recoverable, not restartable

    Durable execution means a failed run resumes from the failed step instead of starting over or leaving partial work behind.

  • Attributable failures

    Traces link an outcome to a specific step and tool call, so debugging is engineering rather than speculation about the model.

  • Measured on tasks, not tokens

    Task-level completion across a fixed set is the metric, which is the one that predicts whether the agent is worth running.

Stack

Technologies we build with

Tools we have delivered production work on, not a capability matrix. We pick per project and will explain the trade-off behind each choice.

Orchestration
  • LangGraph
  • Temporal
  • MCP
Models
  • Claude
  • GPT
  • Llama
Observability
  • OpenTelemetry
  • Structured traces
  • Eval suites
Integration
  • REST
  • Webhooks
  • PostgreSQL
  • Redis
Industries

Who this is for

Agents earn their place where a task spans several systems and currently costs a person an hour of clicking.

  • Financial services
  • Insurance
  • Logistics
  • E-commerce & retail
  • Legal & professional services
  • Healthcare operations
  • SaaS products
  • Manufacturing
Questions

AI Agent Development: common questions

Related reading

What is an AI agent, in practical terms?

An AI agent is a system that receives a goal, plans a sequence of steps toward it, calls tools to carry those steps out, and adapts when a step returns something unexpected. The distinction from a chatbot is action: a chatbot answers, an agent changes something in your systems. That distinction is also why the engineering is different — the interesting problems are permission scoping, failure recovery, and audit, none of which a question-answering system needs.

How do you stop an agent from doing something harmful?

Three mechanisms, applied together. Tools are scoped to least privilege so the agent can only reach what it explicitly needs. Irreversible actions — sending, paying, deleting, publishing — pause for human approval by default, with the reasoning and evidence presented. And every action is logged with its inputs and outputs, so anything unexpected is visible immediately rather than at quarter end. Prompt instructions are not a control; they are a request, and they fail under unusual input.

What happens when an agent fails midway through a task?

We build agents on durable execution, so a run that fails at step four resumes from step four rather than restarting or leaving partial work behind. Each step records what it changed, which makes a rollback possible where resuming is not appropriate. This is the part most agent demos skip entirely, and it is the difference between a system your operations team will run unattended and one that needs a person watching it.

How do you measure whether an agent is working?

Task-level completion across a fixed test set, not per-response scoring. We define a set of representative end-to-end tasks with known correct outcomes, run them on every meaningful change, and track the completion rate alongside cost and latency per task. Per-response quality can look excellent while multi-step completion is poor, which is exactly the failure mode that makes agents look impressive in a demo and unreliable in production.
Related

Services that pair with this

What is the worst thing your agent could do?

That is the first question we will ask, and the answer shapes the whole design. Bring us the workflow you want automated.

Design an AI AgentSee Our Work