What you get
- AI agents running autonomously on defined workflows with measurable reliability
- Clear audit trails and human escalation paths for every agent action
- Hours per day back for the team that used to run the workflow manually
Build
AI agent development
Your team spends 40% of their day on work a good agent handles in seconds.
30+
Agents shipped
70%
Tasks automated
12
Weeks to launch
Trusted by teams at
The Problem
What problem does this service solve?
You've picked the workflows where AI should act for you. But building agents that are reliable, safe, and wired into production systems needs orchestration and evaluation expertise your team hasn't built yet.
Every month you delay, your competitors ship agents that handle customer calls, process orders, and run onboarding while your team does it all manually. The cost isn't just labor. It's the pace at which you fall behind.
Overview
What is AI agent development?
The gap between an impressive agent demo and a production agent your team actually trusts is enormous. We bridge it with structured planning, defined tool boundaries, and human-in-the-loop checkpoints that keep agents reliable.
Most AI agent demos loop forever or invent actions that don't exist. Production agents need structured planning, tool boundaries, memory management, and a clear escalation path when confidence drops.
We build agents as composable systems with defined tool access, state management, and live observability. Every agent ships with human-in-the-loop checkpoints for high-stakes decisions and a full audit trail for every action it takes.
You get agents that run reliably inside your real workflows, not standalone demos that need a senior engineer watching them all day.
Experience Signal
Shipped 30+ AI agents across customer service, SaaS copilots, and operations workflows. 12 weeks is our default, not our stretch goal.
What we build
AI agent development services we deliver
Voice AI agents for phone and support
Outbound and inbound agents that answer calls, qualify leads, and run structured interviews. Powered by ElevenLabs and Whisper, shipped with sentiment and keyword analytics.
In-product copilots for SaaS
Contextual agents that read your data, navigate your UI, and take permission-scoped actions. Users finish workflows they used to abandon.
Customer support agents
First-line agents that resolve order lookups, returns, and account changes. Clean handoff to a human the moment confidence drops.
Back-office workflow agents
Multi-step agents that run client onboarding, ticket triage, and internal coordination across Slack, Jira, and your CRM.
Sales and qualification agents
Agents that run structured qualification calls or chat flows, score leads against your rubric, and sync results straight into your CRM.
Document and research agents
Agents that read PDFs, contracts, and policy docs, pull structured answers, and cite their sources so humans can verify every output.
Multi-agent orchestration
Systems of agents coordinating over shared state, each with its own tools and memory. Useful when one big agent turns into a mess.
Evaluation harnesses and guardrails
Behavior test suites, red-team prompts, and live drift monitoring so you catch regressions before a customer does.
Fit
Is this service right for you?
Good fit
- Teams building AI copilots that take actions inside their product on behalf of users
- Customer support orgs rolling out voice or chat agents for frontline resolution
- Operations teams automating multi-step workflows that need reasoning and judgment
- SaaS companies adding agentic features to differentiate their product
- Enterprises wiring agents into back-office systems their teams spend hours navigating
Not the right fit
- Teams looking for simple chatbot Q&A with no action-taking
- Projects without a defined workflow for the agent to run inside
- Orgs not ready for AI systems that take autonomous actions on live data
Process
How does AI agent development delivery work?
1
Phase 1· Week 1-2
Agent scope and tool design
We define the agent's objectives, tool inventory, decision boundaries, and escalation rules. Every capability gets mapped to a business outcome before architecture starts.
Deliverables
- Agent capability map with tool inventory
- Decision boundary and escalation rules
- Evaluation criteria for agent behavior quality
2
Phase 2· Week 2-4
Orchestration architecture and memory design
We design the planning loop, state machine, memory layer, and tool integration interface. Evaluation harnesses get built before any production code ships.
Deliverables
- Agent orchestration architecture with state machine
- Memory and context management design
- Evaluation harness with test scenarios
3
Phase 3· Week 4-9
Build, integrate, and evaluate
We build the agent, connect tools, instrument behavior tracking, and iterate against evaluation benchmarks with real workflow data every week.
Deliverables
- Production agent with tool integrations
- Behavior tracking and observability dashboard
- Human-in-the-loop review interface
4
Phase 4· Week 9-12
Hardening and production rollout
We stress-test the edge cases, finalize guardrails, deploy to production, and hand over operational controls with a runbook your team can actually use.
Deliverables
- Production deployment with guardrails and rate limits
- Operational runbook for monitoring and incident response
- Post-launch optimization backlog
Outcomes
- AI agents running autonomously on defined workflows with measurable reliability
- Clear audit trails and human escalation paths for every agent action
- Hours per day back for the team that used to run the workflow manually
- A clean pattern your team can reuse to ship the next agent without us
Deliverables
- Agent architecture with orchestration, memory, and tool layers
- Production agent with tool integrations and API connections
- Evaluation suite with behavioral test scenarios
- Human-in-the-loop review and escalation interface
- Observability dashboard for agent actions and performance
Success Metrics
- Agent task completion rate without human intervention
- Escalation rate and false-escalation rate
- Average resolution time vs. the manual baseline
- Action accuracy against the evaluation rubric
- Cost per successful task
Engagement models
12-week end-to-end delivery for one production AI agent, from scoping to rollout.
Best forTeams shipping their first agent for a specific workflow or customer-facing task.
AI models we work with
GPT-5
OpenAI
General-purpose planning and tool use for agents that need broad reasoning.
Claude Opus 4.6
Anthropic
Long-context agents that read large docs, maintain state, and handle multi-step reasoning.
Claude Sonnet 4.6
Anthropic
High-volume production agents where you need smart outputs without the Opus price tag.
Gemini 2.5 Pro
Multi-modal agents that need to read images, charts, or PDFs alongside text.
Llama 3.3
Meta
Self-hosted agents for regulated industries where sending data to a third-party API is a non-starter.
Whisper + ElevenLabs
OpenAI / ElevenLabs
Voice agents - speech to text in, natural-sounding voice out, live on customer calls.
Use Cases
Common use cases for AI agent development
Customer service voice agent
A support team handles 500+ daily calls for order status, returns, and account changes. Hold times hit 8 minutes at peak.
How we build it
We build a voice agent that handles order lookup, return initiation, and account updates through natural conversation. The agent talks to your OMS and CRM via tool calls, and escalates to a human the moment its confidence drops below threshold.
Outcome
60% of inbound calls resolved without a human transfer. Average hold time drops below 2 minutes.
In-product copilot for SaaS
Users get stuck on complex multi-step workflows and file tickets for things the product already does.
How we build it
We build a copilot agent that understands user intent, navigates the product on their behalf, and runs multi-step actions with permission-aware access and a clean undo path.
Outcome
Support ticket volume for workflow questions drops 40%. Underused features finally get discovered.
Workflow orchestration agent
An ops team manually runs client onboarding across Slack, Jira, and internal tools. It eats 3 hours per client, every client.
How we build it
We build an orchestration agent that runs the onboarding checklist, creates tasks in the right systems, chases blockers, and reports status to project leads without anyone asking.
Outcome
Per-client onboarding effort drops from 3 hours to 30 minutes of oversight. Nothing falls through the cracks.
What clients say
Real feedback from real teams
We went from text surveys that nobody finished to AI phone interviews that people actually enjoy. The voice agents handle the whole conversation, and the analytics tell us what we need to know without reading a single transcript.
Cherian Koshy
Behavioral Strategist - USA Today Bestselling Author
I spent years at Amazon fighting static surveys. RaftLabs built a working prototype in four days that already outperformed every survey tool I'd used. Twelve weeks later we had a full SaaS that product teams actually want to use.
Founder
Ex-Amazon PM - Perceptional
Proof
Recent ai agent development work
BuildVoice AI interview platform for Cherian Koshy
12 weeks
To production
Global
Call reach
“Text surveys nobody finished became phone interviews people enjoy.”
Read case studyBuildConversational AI survey agent for Perceptional
4x
Deeper insights
12 weeks
Concept to launch
“Working prototype in four days. Full SaaS in twelve weeks.”
Read case studyBuildInventory agent with AI OCR for 40+ gas stationsRead case study
40+
Stations unified
20K+
Transactions
Industries
AI agent development for your industry
Fintech
KYC agents, fraud triage, and customer service agents that pass compliance review.
ExploreHealthcare
Patient intake, appointment booking, and clinical document agents with guardrails regulated data demands.
ExploreRetail
Inventory agents, price-change orchestrators, and store copilots for staff who used to live in spreadsheets.
ExploreHospitality
Front-desk voice agents, booking copilots, and guest-facing agents for properties short on staff.
ExploreInsurance
Claims triage agents, underwriting assistants, and document-extraction agents that cut handle time without cutting corners.
ExploreMarTech
Voice interview agents, conversational survey agents, and research agents that replace hours of analyst work.
ExploreFrequently asked questions about AI agent development
A chatbot answers questions. An agent plans, reasons, and takes actions. Agents call APIs, run multi-step workflows, hold memory across sessions, and pick which tool to use next. They run autonomously inside the boundaries you set.
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Next Step
Which workflow should your first AI agent handle?
Tell us the repetitive process you want to hand off. We'll scope an agent, set its boundaries, and show you the fastest path to production.