Since 2010
Building Software
Agents That Ship. Not Demos.
AI Agent Development Services
We build AI agents that plan, call tools, and complete real workflows with evaluation and guardrails in place. From single-assistant copilots to multi-agent systems, delivered with fixed pricing and full source-code ownership.
- 120+
- AI Engineers
- 9,000+
- Projects Delivered
- 3,900+
- Apps Published
- 25+
- Countries Served
Trusted by the brands we build for
9,000+
Projects Delivered
3,900+
Apps Published
500+
Engineers
insight
When AI agents are the right tool
An AI agent earns its keep when a workflow involves judgement plus action: reading documents, deciding next steps, calling systems, and reporting back. Chatbots answer questions; agents do work. If your process still needs a human for every step, a good retrieval pipeline may be all you need, and we will say so rather than sell you a framework you don't.
We have put 120+ AI engineers on production systems since 2010 across 9,000+ delivered projects, and we treat evaluation as a first-class deliverable: every agent ships with test scenarios, guardrails, and observability so quality is measured per release. For broader model work, see our AI development services.
What our AI agent development services cover
From a first internal copilot to a supervised multi-agent platform.
Tool-calling agents
Agents that read your systems through typed tools and complete multi-step tasks with permission checks at every boundary.
- ✓Typed tool schemas
- ✓Permission-aware execution
- ✓Retry and fallback logic
- ✓Full action audit logs
Multi-agent systems
Orchestrated specialist agents that hand off work, with a supervisor pattern keeping behaviour predictable under load.
- ✓Supervisor and worker patterns
- ✓State and context handoff
- ✓Cost and latency budgets
- ✓Deadlock and loop guards
RAG-backed knowledge agents
Agents grounded in your private documents with citation, so answers carry evidence instead of confidence alone.
- ✓Document ingestion pipelines
- ✓Hybrid semantic search
- ✓Citation and attribution
- ✓Freshness and re-indexing
Agent evaluation and guardrails
Scenario suites, red-teaming, and output validation that measure agent quality before and after every release.
- ✓Golden task test sets
- ✓LLM-as-judge pipelines
- ✓Output schema validation
- ✓Human-in-the-loop gates
Agent observability
Tracing for every prompt, tool call, and decision, so you can debug behaviour and attribute cost per workflow.
- ✓Trace-level logging
- ✓Token cost tracking
- ✓Drift alerting
- ✓Replay and debugging
Copilot and assistant UX
Interfaces that make agent work reviewable: streaming responses, plan previews, and one-click approvals for consequential actions.
- ✓Streaming UI patterns
- ✓Plan-then-execute flows
- ✓Approval workflows
- ✓Escalation to humans
What every engagement includes
Every AI agent project ships with the following, agreed in writing before the first sprint begins.
Discovery workshop
A structured workshop maps your goals, users, and constraints into a scoped AI agent roadmap with a fixed written quote before any sprint starts.
Named senior team
You get named, senior engineers with CVs you can review, not a rotating bench. The people in the kickoff are the people who ship.
Sprint demos every two weeks
Working builds are demoed live each cycle with CI running on every merge, so progress is visible in software, not slideware.
100% source-code ownership
All AI agent source, configurations, designs, and documentation transfer to you on delivery with no licensing strings or vendor lock-in.
60 days of post-launch support
Engineering time is on standby for two months after go-live to triage defects, tune performance, and stabilise the release at no extra cost.
12 months of free updates
Compatibility updates, security patches, and platform upgrades ship free for a full year after handover.
What you can build with AI agents
Patterns our clients put into production, each scoped to a workflow that pays for itself.
Customer support agents
Agents that resolve tier-one tickets end to end, escalate cleanly to humans, and keep a full audit trail of every action taken.
Back-office automation
Document-heavy processes like claims intake, invoice matching, and KYC checks handled by supervised agents with review queues.
Sales and CRM agents
Lead qualification, enrichment, and follow-up drafting inside your CRM, with guardrails that stop agents sending anything unreviewed.
Internal knowledge copilots
Agents that answer policy, product, and process questions from your own documentation, with citations to the source.
Data and ops agents
Agents that monitor pipelines, investigate anomalies, and draft incident summaries for on-call engineers.
How a AI agent Project Runs
Six stages, each with a sign-off gate before the next begins.
- 01
Discovery
We map your agent goals, users, and constraints in a structured workshop, ending in a fixed written quote and an architecture risk list.
✓ step 01 complete - 02
Solution design
The AI agent architecture, integrations, and delivery plan are designed and reviewed before build work starts, so scaling later is not a rewrite.
✓ step 02 complete - 03
Foundation sprint
Repositories, CI, environments, and a walking skeleton are set up first. Something real runs on a staging URL before feature sprints begin.
✓ step 03 complete - 04
Sprint delivery
Two-week sprints with live demos every cycle. AI agent work is reviewed, tested, and merged continuously, so scope surprises surface early.
✓ step 04 complete - 05
Hardening and launch
Performance, security, and accessibility checks run against agreed budgets, followed by a staged agent launch with monitoring from hour one.
✓ step 05 complete - 06
Support and handover
60 days of included support, 12 months of free updates, and documentation that lets your internal team own the system with confidence.
✓ step 06 complete
The stack behind our agent builds
Default choices, stated with reasons.
Orchestration
Framework-light orchestration built on typed tool contracts and explicit state machines, avoiding framework lock-in where it adds risk.
Models
Frontier LLMs via managed APIs for reasoning, with smaller distilled models for high-volume steps where latency and cost matter.
Retrieval
Vector and keyword hybrid search over chunked documents with metadata filters, chosen per corpus rather than one-size-fits-all.
Infrastructure
Queues, sandboxed tool execution, and secrets management so agents run with least privilege and every action is replayable.
How intelligence flows through the system
What the model reads, and what it is allowed to change.
Task planner
Decomposes a user request into steps and picks tools.
Reads
Request, user role, workflow history
Changes
Plan state and next-step selection
Tool executor
Calls business systems through typed, permission-checked interfaces.
Reads
CRM, ERP, ticketing, and document APIs
Changes
Records and drafts, gated by approvals
Quality monitor
Scores outputs against evaluation suites and routes failures to humans.
Reads
Traces, evaluation results, user feedback
Changes
Escalation queue and alert thresholds
What's included, standard
The baseline on every AI agent engagement. Not an upsell tier, just the floor.
Fixed written quote
The price agreed at discovery is the price you pay, with change handled through explicit scope changes rather than silent drift.
Solution architecture document
System design, data flows, and technology decisions written down so your team can audit and own the result.
Dedicated project manager
One accountable point of contact with real authority over the delivery, not a message relay between time zones.
QA on every build
Automated and manual testing on each AI agent release candidate, with defects triaged before anything reaches production.
Security review
A checklist-based security pass covering authentication, data handling, and dependency vulnerabilities before launch.
CI/CD and environments
Pipelines, staging, and preview environments configured from week one, so releases are routine rather than dramatic.
Analytics and monitoring
Instrumentation wired before launch so you can see real user behaviour and system health from day one.
Documentation and handover
Code comments, runbooks, and architecture notes written for your next developer rather than for our convenience.
Model strategy session
A working session on model selection, cost curves, and evaluation design for your use case, even mid-engagement.
Case studies that speak volume
Etihad Airways
A high-traffic consumer platform our team ships and operates at national-carrier scale, the same bar every build is held to.
1M+
Monthly Users
40%
Engagement Lift
PragPlay
A consumer platform from the same engineering system, proving the infrastructure core holds under real-time load with strict uptime demands.
500K+
Players
99.9%
Uptime
Marvel Studios
A high-volume consumer experience our team ships at global-release scale, the same discipline applied to every launch.
10M+
Streams
65%
Completion
/11
Why teams pick us for AI agent work
The differences live in engineering habits, not pitch decks.
120+ AI engineers
A deep bench of engineers who have shipped AI to production, not a generalist team experimenting with prompt templates.
Evaluation-first delivery
Every agent ships with test scenarios and guardrails, so you know the quality level per release instead of discovering it from users.
No framework worship
We pick orchestration per problem, and we will tell you when a plain retrieval pipeline beats an agent on cost and reliability.
Least-privilege by default
Agents run with scoped permissions, sandboxed tools, and human approval gates on consequential actions, designed in from day one.
Track record since 2010
9,000+ projects and 3,900+ apps across 25+ countries, with production AI systems among them at 500+ engineers strong.
AI agent development FAQs
$ moxace launch --nowReady to Put an Agent to Work?
Talk to our AI agent team about a fixed-scope build with evaluation, guardrails, and full source-code ownership included.
- 01
Share your idea
a quick call, no deck required
- 02
See the live demo
every screen, admin to user
- 03
Launch with source
full code, your brand, your servers
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