Unpredictable step selection
The same goal can lead an agent down different paths, including wrong or wasteful ones.
AI & Automation · AI Agents
AI agent development services with guardrails you define
We build agents that can research, look things up, fill in records and prepare actions across your systems, while consequential steps wait for a person to approve them and every action is logged.
What is AI Agents?
AI agent development services build software in which a language model works toward a goal by deciding which steps to take and which tools to call, such as searching documents, querying a database, updating a record or drafting a message, and then using the results to decide its next step. Unlike a fixed workflow, the sequence isn't scripted in advance. That flexibility makes agents useful for open-ended tasks and also less predictable, so they need tightly scoped permissions, approval gates and monitoring.
Agentic AI is often presented as autonomous digital staff, but current models still misread instructions, choose the wrong tool, loop on a failing step or act on manipulated input. Giving such a system broad access to email, finance or customer data without limits is a real operational risk. Agents are worth building where tasks vary too much for a fixed workflow, the tools can be tightly scoped, and a person approves anything that's hard to undo.
Why agents need tighter guardrails than other AI
Excessive permissions
An agent with write access to everything can do damage quickly when it misunderstands a task or is manipulated.
Errors compounding over steps
A small mistake early in a long task can carry through every later step unless intermediate results are checked.
Cost and latency
Multi-step reasoning makes many model calls per task, which adds time and cost compared with a single request.
Our AI Agents service
Task and suitability assessment
Deciding whether a task truly needs an agent or would be better as a fixed workflow, and defining the goal, boundaries and success criteria.
Tool design
Narrow, well-documented tools such as search, read a record or draft an order, each with input validation and the minimum permissions it needs.
Approval gates
Points where the agent pauses and presents its proposed action, such as sending an email or creating a payment, for a person to approve, edit or reject.
Orchestration and limits
Control logic that caps steps, time and spend per task, handles tool failures and stops the agent when it isn't making progress.
Activity logging and review
A full trace of the agent's reasoning summary, tool calls, inputs and outputs, viewable by staff for each task.
Evaluation scenarios
A suite of realistic tasks, including tricky and adversarial ones, run before each change to check outcomes, safety and cost.
Included capabilities
- Goal-driven multi-step task execution
- Scoped tools with least-privilege access
- Human approval before consequential actions
- Read-only mode for early rollout
- Step, time and cost limits per task
- Full trace of tool calls and results
- Escalation to staff when stuck
- Scenario-based evaluation suite
- Prompt injection safeguards on retrieved content
What AI Agents changes for your business
Handles varied tasks
Agents can work through requests that don't follow the same steps every time.
Preparation without loss of control
Staff review proposed actions instead of doing all the gathering and drafting themselves.
Transparent behavior
Every tool call is logged, so it is clear what the agent did and why a result was reached.
Autonomy earned gradually
Approval gates are removed only where evidence supports it.
Where it makes the biggest difference
Research and briefing
Gathering information about a customer, supplier or tender from internal records and approved sources, and producing a briefing for staff.
Order and account investigation
Tracing why an order is delayed by checking sales, stock, purchasing and shipping records, then proposing a resolution for approval.
Back-office preparation
Preparing draft quotations, purchase requests or record updates from an emailed request, held for approval before submission.
Internal IT and operations helper
Diagnosing common access or configuration requests using read-only tools, and handing actions that change systems to staff.
Eight steps from task assessment to supervised autonomy
Agents start in read-only or approve-everything mode, and approvals are removed one action at a time based on what the pilot shows.
Assess
Confirm that the task varies enough to need an agent, and define goals, boundaries and what counts as success.
Design tools
Specify each tool, its permissions and validation, starting with read-only access wherever possible.
Define approvals
Agree which actions need human approval and who approves them.
Build
Implement the agent loop, tools, limits and logging on a staging environment with test data.
Evaluate scenarios
Run realistic and adversarial tasks, review traces and fix failure patterns.
Supervised pilot
Run with a small group, in read-only or approve-everything mode, and review each trace.
Relax gradually
Remove approvals only for actions where the pilot shows consistent, low-risk results.
Monitor
Track outcomes, escalations and cost per task, and re-evaluate when models or tools change.
What it connects to
AI Agents
- Odoo ERP and CRM via API
- Email and calendar systems
- Document stores and search indexes
- Helpdesk platforms
- Internal databases
- Slack and Microsoft Teams for approvals
- Hosted or self-hosted language models
- Model Context Protocol (MCP) tool servers
Built with
AI agent or AI workflow
| Criterion | AI workflow automation | AI agent |
|---|---|---|
| Who decides the steps | Designed in advance | The model, within its tools |
| Predictability | High | Lower; varies by task |
| Cost per task | Lower, fewer model calls | Higher, many model calls |
| Oversight | Review of uncertain items | Approval gates and trace review |
| Best for | Repeatable processes with messy inputs | Varied, multi-step tasks that need investigation |
Security, quality and scale
Least-privilege tools
Each tool has its own scoped credentials, and agents never receive general administrator access.
Human approval for irreversible actions
Payments, external messages, deletions and similar actions require explicit approval by default.
Untrusted input handling
Content from emails, websites and documents is treated as data, and instructions within it can't grant new permissions.
Kill switch and limits
Per-task limits on steps and spend, plus the ability to pause an agent immediately.
What working with us looks like
We'll suggest a workflow if it fits
Many tasks described as agent work are better served by a fixed AI workflow, and we'll say so.
Permission design first
We define what the agent can touch before we write the agent itself.
Deep system integration
Our team integrates with Odoo and custom systems, so tools are built carefully against real APIs.
Realistic about autonomy
We start supervised and expand autonomy only where pilot results justify it.
What it costs
Fixed-price packages with the scope written down, paid stage by stage. Your project goes live once it's fully paid.
Related services, technologies and industries
Sources and further reading
Official documentation and guidance if you want to read more about this topic.
AI Agents: questions we hear most
What is the difference between an AI agent and an automated workflow?
An automated workflow follows steps designed in advance, while an AI agent decides its own next step and which tool to use based on the goal and the results so far. Agents are more flexible but less predictable, so they need more safeguards.
Can AI agents act completely on their own?
They can technically, but we don't recommend full autonomy for consequential actions with current models. We design agents to prepare and propose actions, require approval for anything hard to undo, and relax approvals only where a pilot shows consistent results.
What stops an agent from doing something harmful?
Agents can only use the tools we give them, each with scoped permissions and input validation. Consequential actions need human approval, tasks have step and cost limits, and content from external sources is treated as data, so it can't grant the agent new permissions.
How much does it cost to run an AI agent?
Running cost depends on how many steps and model calls each task takes, the model used and task volume. Agents generally cost more per task than single model calls, so we measure cost per task in the pilot and set limits.
Which models are used for AI agents?
Agents need models that are reliable at following instructions and calling tools, typically hosted models from providers such as OpenAI, Anthropic or Google, though open-weight models can work for narrower tasks. We choose by running the agent's evaluation scenarios on candidate models.
How do we see what the agent did?
Every task produces a trace showing each tool call, its inputs and outputs, and the outcome. Staff can review traces in an admin view, and approval requests show the information the agent used to reach its proposal.
Next step
Describe the task you'd hand to an agent
Tell us the task, the systems involved and what a good outcome looks like. We'll assess whether an agent or a simpler workflow fits, and where approvals belong.