Which workflows suit AI agents best?

High-volume, text- and data-heavy work with clear policies: ticket resolution, invoice and claims processing, lead research, report generation, onboarding and IT triage are common starting points.

Can agents work with our existing systems?

Yes. Agents connect through APIs, databases, MCP servers and integrations with tools such as Salesforce, SAP, NetSuite, Slack and your ticketing platform.

What is a multi-agent system?

It is a group of specialised agents, for example a planner, researcher, executor and reviewer, coordinated by a supervisor. It suits long or branching tasks where one agent would lose focus.

Can agents talk to customers?

Yes, by chat, email or voice, within the boundaries you set. Sensitive actions can require approval and any conversation can be handed to a person with full context.

Will agents replace our staff?

The aim is to remove repetitive work and speed up outcomes. Most teams use agents to handle the routine majority so people can focus on exceptions, relationships and decisions.

Which models and frameworks do you use?

We are model-agnostic and choose per task from leading commercial and open models, with frameworks such as LangGraph, CrewAI, AutoGen or a custom orchestrator, depending on your needs.

How do agents remember things?

Through short-term working memory for the current task and long-term memory in vector databases or structured stores, so agents can recall past cases, preferences and your documents.

How do you stop agents from hallucinating or going off-script?

With grounding in your data, structured outputs, validators, reviewer agents, confidence thresholds and approval gates, plus evaluation suites that catch regressions before release.

What is the Model Context Protocol (MCP)?

MCP is an open standard for connecting AI models to tools and data sources. Using it lets us add or swap integrations without rewriting the agent.

How do you measure agent quality?

We build evaluation datasets from your real cases and track task success, accuracy, latency, cost per task and escalation rate, before launch and continuously after.

Is it safe to let an agent take actions?

Yes, when designed for it. We use least-privilege access, spend and rate limits, approval steps for sensitive actions, sandboxing and full logging so every action can be reviewed or reversed.

Where does our data go?

We can deploy in your cloud or a private environment, redact personal data, and use model providers under terms that do not train on your data. Region and retention rules are set with you.

Can we audit what an agent did?

Yes. Every step, tool call, input and output is traced and replayable, which supports internal audits and regulatory reviews.

Do you support regulated industries?

We work with healthcare, finance and insurance teams and design controls to align with frameworks such as GDPR, HIPAA and SOC 2. Formal compliance remains with your organisation and its assessors.

Who owns the agents and the data?

You do. Prompts, code, evaluation sets and trained assets are yours and fully documented for your teams.

How long does a first agent take?

A focused pilot usually takes 6 to 10 weeks from discovery to supervised live use. Broader multi-agent programmes are phased over several months.

How much does agentic AI cost?

It depends on workflow complexity, integrations, volume and model usage. After discovery we give a fixed-scope estimate for the pilot and a clear view of run costs per task.

How do we control running costs?

Through model routing, caching, smaller specialised models, per-task budgets and monitoring dashboards, so cost per outcome stays visible and predictable.

Yes, and we recommend it. Start with one workflow, prove the measured value, then extend autonomy and add agents.

What happens after go-live?

We monitor performance, review failures, tune prompts and tools, add integrations and upgrade models as the agent takes on more work.

Agentic AI Solutions

AI agents that plan, act and deliver outcomes

Aarvexa builds autonomous AI agents that understand a goal, use your tools and data, check their own work and ask a human when it matters, so whole workflows run themselves.

Reasoning agents
Tool & API use
Human-in-the-loop

Talk to our experts See how it works

LIVEAGENT RUN01 / 07

PlanActVerifyLearn

Run progress

Agent step

Goal received

Tasks today

1,284

86% auto2.4s

Confidence

Policy checks pass

1to review

Autonomous AgentsMulti-Agent SystemsTool & API UseAgentic RAGWorkflow AutomationHuman-in-the-LoopGuardrails & EvalsAgent MemoryMCP IntegrationsVoice AgentsProcess MiningObservabilityAutonomous AgentsMulti-Agent SystemsTool & API UseAgentic RAGWorkflow AutomationHuman-in-the-LoopGuardrails & EvalsAgent MemoryMCP IntegrationsVoice AgentsProcess MiningObservability

What we build

AI agents for every team that runs on workflows

From the support queue to the finance close, we build complete agents that finish the work, not just demos.

Service

Customer Support Agents

Agents that read the ticket, check the order, issue the refund and reply, escalating only when judgement is needed.

Operations

Workflow & Back-Office Agents

Invoice matching, onboarding, claims and reconciliations run end to end across your ERP, CRM and inboxes.

Growth

Sales & Revenue Agents

Lead research, outreach drafting, CRM hygiene and meeting prep handled while your team sells.

Analytics

Data & Analyst Agents

Ask a business question in plain language; the agent queries, charts and explains with sources.

DevOps

Engineering & IT Agents

Incident triage, runbooks, code review and access requests with every action logged and reversible.

Orchestration

Multi-Agent Systems

Specialist agents (researcher, planner, executor, reviewer) coordinated to tackle work no single prompt can.

Voice

Voice & Conversational Agents

Natural phone and chat agents that book, qualify and resolve, with a smooth hand-off to people.

Governance

Compliance & Risk Agents

Policy checks, document review and audit-trail generation for regulated teams.

Live pipeline

How an AI agent plans, acts and checks its work

Five stages turn a goal into a verified outcome. Hover to hold a stage, click to jump.

Step 01 of 5

Goal

A person, schedule or event hands the agent an outcome to achieve, not a script to follow.

Natural-language goals
Triggers from email, tickets, APIs
Clear success criteria

Outcome in, no script needed

Where it applies

One agent platform, six ways to put it to work

Pick the programme that matches your business. We reuse the same proven planning, memory and tool core.

Resolve tickets, not just answer them

Agents pull the order, apply policy, take the action and write the reply, handing the tricky ones to your team with full context.

Order, refund and account actions
Policy-aware replies with citations
Smart escalation with a summary
24/7 multilingual coverage

Plan this project

TodaySupport agent

78%auto-resolved

Tickets handled412
Avg. resolve1.8 min
Escalated9%

Open run log

Live agent running

Every step traced

Why Aarvexa

Agents you can trust with real work

Handing work to an agent only pays off if it is accurate, controllable and cheap to run. We pair modern models with engineering discipline: evaluation, guardrails and observability from day one.

Who we work with

Customer support teamsFinance & operationsSales & marketingHealthcare & insuranceRetail & e-commerceSoftware & IT teams

Autonomy with control

Permissions, approval gates, spend limits and kill-switches mean agents act boldly where safe and ask where it matters.

Measured, not magical

We build evaluation sets from your real cases, so you see accuracy, cost per task and failure modes before go-live.

Fits the stack you have

Agents connect to your CRM, ERP, ticketing, data warehouse and internal APIs, including through MCP, without a rebuild.

Pilot to production

One team takes you from a scoped pilot to monitored production, with observability and ongoing tuning.

Technology

A stack built for agents, proven in production

We choose the right tool for each layer, from the model to the monitoring dashboard, and swap parts when your platform changes.

Models & reasoning

ClaudeGPTGeminiLlamaMistralFine-tuned small models

Agent frameworks

LangGraphCrewAIAutoGenOpenAI Agents SDKSemantic KernelCustom orchestrators

Tools & integration

Model Context ProtocolREST / GraphQLZapier / n8nSalesforceSAP / NetSuiteSlack / Teams

Memory & knowledge

pgvectorPineconeWeaviateElasticsearchRedisKnowledge graphs

Safety & observability

GuardrailsLangSmithOpenTelemetryEval harnessesPII redactionRole-based access

Control & trust

Autonomy with safety, security and control

An agent is judged by what happens when things go wrong. We design for that from day one.

Least-privilege access

Agents get scoped, revocable credentials and can only touch the systems and records their job requires.

Human approval where it counts

Refunds, deletions, outbound messages and other sensitive actions can be routed to a person before they run.

Your data stays yours

Private deployments, PII redaction, no training on your data by default, and region-aware storage.

Full traceability

Every thought, tool call and result is logged and replayable, which makes audits and debugging straightforward.

Our process

From first idea to an agent in production

Indicative timelines for a typical pilot. We confirm yours after discovery.

01Week 1

Discover & pick the workflow

We map candidate processes and choose one with clear volume, rules and a measurable outcome.

02Week 1–3

Connect data & tools

Secure access to your knowledge, APIs and systems, with permissions designed up front.

03Week 2–5

Build the agent

Prompts, planning, memory and tools assembled and tested against real examples.

04Week 4–7

Evaluate & harden

Evaluation sets, red-teaming, guardrails and approval gates until quality and cost targets are met.

05Week 6–10

Pilot with your team

Shadow mode, then supervised live use, with reviewers' feedback fed straight back in.

06Ongoing

Scale & improve

Monitoring, cost control, new workflows and model upgrades as the agent earns more autonomy.

FAQ

Frequently Asked Questions

Straight answers on agentic AI: applications, technology, safety, cost and timelines.

Agentic AI refers to AI systems that pursue a goal on their own: they plan steps, use tools and data, check results and adapt, instead of only answering a single prompt. Think of it as software that can do a task, not just describe it.

A chatbot replies to messages. An agent takes actions: it looks things up, updates systems, sends messages and completes multi-step work, while a chatbot usually stops at the answer.

We design, build and run AI agents and multi-agent systems: use-case selection, data and tool integration, evaluation, guardrails, deployment and ongoing monitoring.

Teams with repetitive, rules-and-judgement workflows such as support, finance operations, sales operations, IT and compliance, in organisations of any size.

Book a free consultation. We review your workflows, pick the best candidate and propose a scoped pilot with clear success metrics.

Let's build it

Ready to put AI agents to work?

Book a free consultation. We will show a live agent demo and map out a pilot you can test on your own workflows and data.

Book a free consultation Explore our AI services