AI Agents

AI Agent Development for Real Multi-Step Work

An AI agent does more than answer. It plans a task, calls your tools and APIs, checks the result and continues until the job is done or a person needs to decide. We design agents with narrow permissions, explicit approval gates and full audit trails, so they can take real work off your team without taking control away from it.

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Bounded autonomy

Each agent has a defined job, a limited toolset and clear rules for when to stop and ask.

Approval where it matters

Actions that spend money, contact customers or change records wait for a person to confirm.

Fully traceable

Every model call, tool call and decision is logged so any run can be reviewed step by step.

What you get

AI Agents: what is included

Tool and API Design

We wrap your systems as well-described tools with strict input schemas and validation, so the model can call them reliably and cannot send malformed requests.

Orchestration and State

Long tasks run as durable workflows with checkpoints, retries and timeouts, so an agent can pause for approval or recover from a failed step without starting over.

Human-in-the-Loop Approval

High-impact actions are routed to a reviewer who sees the proposed step, the reasoning and the evidence, then approves, edits or rejects it.

Least-Privilege Access

Agents run with scoped credentials, read-only access by default, spending and rate limits, and an allowlist of the actions they are permitted to take.

Evaluation Harness

We test agents against scripted scenarios in a sandbox, scoring task completion, tool choice and policy compliance before and after every change.

Tracing and Audit Logs

Complete run traces show each prompt, tool call and result, giving your team the record it needs for debugging, review and accountability.

What AI agent development involves beyond a clever prompt

An agent is a loop. The model reads the goal, decides which tool to call, receives the result and decides again. The quality of that loop depends far more on engineering than on the prompt. Tools need precise descriptions and strict schemas. Results need to be returned in a form the model can reason about. Errors need to be surfaced clearly so the agent can retry or change approach instead of repeating the same failed call.

We also decide how much autonomy the task really needs. Many processes are better served by a fixed workflow with a model making one or two judgment calls than by a fully open-ended agent. Where open-ended planning is justified, we add step limits, budget limits and stopping conditions. For larger jobs, an orchestrating agent can delegate to specialized subagents, each with its own tools and a narrow brief, which keeps behavior predictable and easier to test.

Strictly typed tool definitions
Durable state with checkpoints and retries
Step, time and spending limits
Sandboxed test environment for every agent
Rollback paths for reversible actions

Keeping autonomous agents safe, observable and under control

An agent that can act can also act wrongly, so safety is built into the architecture rather than added as a warning in the prompt. We classify every tool by impact. Reading data is usually automatic. Drafting is automatic. Sending, paying, deleting and changing permissions require approval until the agent has earned trust on that action, and some stay gated permanently. Content the agent reads from emails or web pages is treated as untrusted input and cannot grant itself new instructions.

Observability is the other half. Each run produces a trace your team can open: what the agent was asked, what it retrieved, which tools it called and why it stopped. We track completion and escalation patterns over time and replay real runs against new model versions before upgrading. Model choice stays flexible. Hosted models from OpenAI or Anthropic handle complex planning well, and open-weight models can run privately for sensitive or high-volume steps.

How we work

A clear process from first call to launch

01
Map the Task

We document the process step by step, including systems touched, decisions made, exceptions and which actions carry real risk.
02
Design Controls

We define tools, permissions, approval gates and stopping conditions before writing any agent logic or connecting production systems.
03
Build and Test

We build the agent in a sandbox and run it through scripted scenarios, edge cases and adversarial inputs.
04
Roll Out Gradually

We start with every action reviewed, then relax approvals step by step as measured performance supports it.

Use cases

Where AI Agents delivers value

Sales Research and Outreach

Research an account, enrich the CRM record, draft a tailored first email and queue it for a rep to approve.

Support Case Resolution

Diagnose a ticket, check order and account data, apply an allowed fix, and escalate anything outside policy.

Operations Coordination

Track open orders, chase missing information from suppliers by email, and update your ERP once details are confirmed.

Finance Reconciliation

Match invoices to purchase orders and payments, flag discrepancies with evidence, and prepare entries for a controller to approve.

FAQ

AI Agents — frequently asked questions

What is the difference between an AI agent and a chatbot?

A chatbot answers questions in a conversation. An agent completes tasks. It plans steps, calls tools and APIs, checks what happened and keeps going until the work is finished or it needs a decision from a person. Many agents have no chat interface at all and are triggered by an event such as a new ticket or email.

Can an AI agent take actions without human approval?

Only where you decide it should. We classify each action by impact. Low-risk steps such as lookups and drafts run automatically. Actions that send messages, move money or change records wait for approval. As the agent builds a measured track record, you can choose to automate more, one action type at a time.

Which systems can an AI agent connect to?

Any system with an API, database access or a reliable interface. That commonly includes CRMs, ERPs, helpdesks, email, calendars, spreadsheets, accounting tools and internal applications. We wrap each one as a tool with a strict schema and scoped credentials, and can expose them through the Model Context Protocol for reuse across agents.

How do you stop an agent from making costly mistakes?

Through layered controls. Agents get the minimum permissions required, hard limits on steps and spending, approval gates on high-impact actions, and validation on every tool input. We test in a sandbox against realistic scenarios before production, and every run is traced so problems are caught and corrected quickly.

How long does it take to build an AI agent?

It depends on the number of systems involved and how risky the actions are. A focused agent with a few well-documented integrations moves quickly from prototype to supervised pilot. Agents that touch financial or customer-facing actions take longer, because integration work, testing and gradual rollout matter more than the agent logic itself.

Related services

Tell us what you want to build

Share your goals and we will reply with a clear plan, timeline and quote — usually within one business day.

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