Service · AI Agent Development
AI Agent Development Services
AI agents that do the work, not just talk about it.
We build AI agents that read, reason, call your tools and complete multi-step tasks across your CRM, booking engine, EHR, LMS or ERP, safely, observably and with a human in the loop wherever you want one.
Chatbots answer questions. Agents finish jobs. A modern AI agent can look up a booking, check a policy, draft the change, ask for approval and update three systems, in the time it takes a person to open the first screen. The hard part isn't the model — it's reliable tool use, permissions, error handling, cost control and knowing when to stop and ask a human. That's the engineering Octobit8 does.
Who is this for
Common profiles that get the most out of this service.
Task agents
Single-purpose agents that own one workflow end to end, such as refund processing, lead qualification or appointment rescheduling.
Multi-agent systems
A coordinator agent that delegates to specialist agents for research, drafting, checking and execution.
Operations copilots
Agents that sit beside your staff, prepare the work and wait for a click to approve.
Back-office automation agents
Agents that process emails, PDFs, forms and spreadsheets and enter the results into your systems.
Research and analysis agents
Agents that gather information from internal and web sources and produce structured reports.
Coding and data agents
Agents that write SQL, generate reports, or assist engineers inside your repositories.
What sets it apart
Principles we apply on every engagement—so results are measurable, not just delivered.
Tool and API integration
Function calling and Model Context Protocol (MCP) servers that connect agents to your internal APIs, databases and SaaS tools.
Planning and memory
Multi-step planning, short-term working memory and long-term memory of users and cases.
Human-in-the-loop controls
Approval steps, confidence thresholds and escalation rules you configure per action.
Guardrails and permissions
Role-based access, action allow-lists, spend limits, PII redaction and prompt-injection defences.
Observability
Full traces of every step, tool call and decision, so you can audit what the agent did and why.
Evaluation
Scenario test suites that measure task success rate before each release.
Capabilities & deliverables
Concrete workstreams we plan, execute, and hand over with documentation and dashboards.
Task agents
Single-purpose agents that own one workflow end to end.
- ▸Refund and returns processing
- ▸Lead qualification and routing
- ▸Appointment rescheduling
Multi-agent systems
A coordinator that delegates to specialist agents.
- ▸Research → draft → check → execute pipelines
- ▸Specialist agents per domain or system
- ▸Shared memory across the pipeline
Operations copilots & back-office automation
Agents that prepare work for a human click, or process documents end to end.
- ▸Approval-queue copilots
- ▸Email, PDF and spreadsheet processing
- ▸Structured research and reporting agents
What you get
Concrete deliverables from this engagement.
A production agent deployed in your cloud
Integration code and MCP servers for your systems
An evaluation suite and a baseline success-rate report
Monitoring dashboards and audit logs
Documentation, runbooks and team training
Additional benefits
How we work with you
A phased approach with clear artifacts—so stakeholders see progress weekly, not only at launch.
Workflow mapping
We document the task as it's done today, its systems, edge cases and the cost of a mistake.
- Workflow map
- Edge-case inventory
- Risk tier
Autonomy design
We decide which steps the agent does alone, which need approval, and which stay human.
- Autonomy matrix
- Approval rules
- Escalation policy
Prototype
A working agent on your sandbox systems within weeks, tested against real past cases.
- Working prototype
- Eval set v1
- Demo to stakeholders
Production hardening
Guardrails, retries, logging, monitoring dashboards and a staff-facing interface.
- Monitoring dashboards
- Audit logs
- Staff UI
Rollout
Shadow mode first, then gradual autonomy as success rates are proven.
- Shadow-mode report
- Autonomy increase plan
- Handover docs
Use cases by industry
Where teams in our focus industries are already applying this service.
Travel & Hospitality
- ▸Booking-change agent that checks fare rules, rebooks and sends the new itinerary
- ▸Vehicle and driver allocation agent for car rental and outstation cab operators
- ▸Group-travel quote agent that builds packages from supplier inventory
Healthcare
- ▸Prior-authorisation and insurance-claim preparation agent
- ▸Appointment scheduling and reminder agent that works across doctors' calendars
- ▸Referral and lab-report triage agent that routes documents to the right team
EdTech
- ▸Admissions agent that screens applications and answers follow-ups
- ▸Course-operations agent that schedules classes, tracks attendance and nudges learners
- ▸Grading-assistant agent that pre-scores assignments for teacher review
Stack & integrations
Representative tools—we meet you where your stack already lives and document every handoff.
Models & orchestration
- —Claude, GPT, Gemini, Llama, Mistral
- —LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK
- —Custom orchestration
Tools & integration
- —Model Context Protocol (MCP)
- —AWS Bedrock Agents, Microsoft Foundry Agent Service
- —Vertex AI Agent Builder
Data & memory
- —Python, TypeScript
- —PostgreSQL, Redis, vector databases
Observability
- —LangSmith
- —Langfuse
- —OpenTelemetry
Good to know
Safety by default
- ✓Starts in read-only or approval mode; autonomy widens only once measured accuracy justifies it
- ✓Every action is logged and most are reversible by design
- ✓High-impact actions require human approval; low-confidence cases escalate
No clean API? No problem
- ✓Database connectors, file drops and email parsing where no API exists
- ✓Browser automation as a last resort
- ✓Legacy and on-premise systems included in scope during discovery
Engagement models
Discovery & Assessment
Fixed fee, 1–2 weeks, written recommendation and roadmap — for teams unsure where an agent fits.
AI Agent Pilot
Fixed scope and price, one workflow in 4 weeks, proving value before a larger budget.
Dedicated Team / FDE
A named engineer or pod on a monthly retainer for an ongoing agent programme.
Starter offer
AI Agent Pilot
4 weeks · [₹ / $ amount — confirm before publishing]
One workflow, one agent, running on your sandbox data. Includes workflow mapping, a working agent with approval steps, a test report on [50] real past cases, and a production plan. If the pilot doesn’t hit the agreed success rate, you don’t pay for the production plan.
Frequently asked questions
Is it safe to let an AI agent take actions in our systems?▼
What if the agent makes a mistake?▼
Do we need clean APIs for everything?▼
How long does it take?▼
Ready to talk specifics?
Share your goals, timelines, and stack—we will propose a scoped next step.