Frequently Asked Questions: AI Agents & Agentic Automation
Get answers on how agentic AI, voice agents, and automation systems work—and why they're the future of business.
What is Agentic AI?
Agentic AI makes autonomous decisions using tools and reasoning; automation follows pre-set rules.
Automation = mechanical (if A → then B). Agentic AI = intelligent (what's the goal → what tools do I have → what's the best step → did it work?).
Example:
- Automation: Lead arrives → Email template sent
- Agentic: Lead arrives → Check firmography → Enrich data → Draft personalized email → Send → Log in CRM → Schedule follow-up
Why it matters: Agents adapt to edge cases. They handle 80% of exceptions without human help.
MCP is a standardized tool interface that works with any LLM; APIs are one-to-one connections.
Traditional APIs = hardcoded integrations (CRM API, Slack API, etc.). MCP = universal tool protocol that any AI model can speak.
Future benefit: Today you use Claude. Tomorrow you might use GPT-5. With MCP, your agents keep working—no rewiring needed.
Analogy: APIs are like phone lines to specific houses. MCP is like a universal phone system.
Yes, with guardrails—agents can escalate, retry, or ask for human help when uncertain.
M AI agents are built with fallback layers:
- Confident? Execute (80% of cases)
- Unsure? Log + escalate to human (15% of cases)
- Error? Retry + fallback strategy (5% of cases)
Real example: A customer support agent gets an angry customer. It can:
1. Try to resolve with empathy
2. If sentiment dips below threshold → escalate to human agent with context
Claude 3.5 Sonnet (primary), GPT-4 (backup), Gemini Pro (integrations)—we test and upgrade as new models release.
We're model-agnostic. Your agent isn't locked into Claude. When a better model launches, we upgrade—free of cost to you (because you're on a Scale partnership).
Benchmarks:
- Reasoning: Claude 3.5 Sonnet
- Speed: GPT-4 Turbo
- Cost: Gemini 2.0
- Multimodal: We test all 3 quarterly
Chatbots answer questions; agents take actions (book meetings, send emails, update systems).
- Chatbot: "What's our return policy?" → "Returns accepted within 30 days."
- Agent: Customer asks "I want to return this" → Agent processes return, generates label, updates inventory, notifies warehouse
Agents can:
- Use tools (integrations)
- Make decisions autonomously
- Take irreversible actions (with guardrails)
- Learn from outcomes
Voice Agents
Sub-500ms response time (sounds natural); most calls have < 1 second round-trip.
Human conversation has ~800ms average delay (pauses are natural). Our voice agents respond in 300–600ms, making them sound human.
Why it matters: >1 second lag makes calls feel robotic. Callers hang up.
Technical: We use streaming LLMs + low-latency APIs. Not batch processing.
Yes—they understand context, can transfer to humans, and schedule follow-ups autonomously.
A voice agent for property inquiries can:
1. Answer initial questions (location, price, availability)
2. Qualify lead (budget, timeline, requirements)
3. Check calendar and book a viewing
4. Take a message if they can't book
5. Send confirmation SMS + link
Multi-turn: Agents remember context (caller said they're first-time buyers → adjust messaging)
Included in Pilot ($8–15K) or monthly retainer ($3–8K/month); no per-minute surcharge for M AI.
Voice infrastructure cost (actual LLM + Twilio calls) = ~$0.10–0.30 per minute. M AI includes this in your package—no surprise bills.
If you get 1,000 inbound calls/month:
- Infra cost: ~$300–900
- Our margin: Build, monitor, improve agent
- Your total cost: Included in retainer
Transparent: You see call logs, cost breakdowns, ROI in your dashboard.
Yes—voice agents pull lead data from Salesforce/HubSpot, check availability in Google Calendar, and update records post-call.
A voice agent answers a sales call:
1. Recognizes caller (Salesforce lookup)
2. Pulls previous interactions
3. Pitches relevant products
4. Checks availability in calendar
5. Books meeting + sends confirmation
6. Updates CRM with notes + sentiment
Integrations we support: Salesforce, HubSpot, Google Calendar, Slack, Zendesk, Microsoft Teams, custom APIs.
Process & Timelines
60–90 min discovery → 10–14 day sprint → live agent (4–6 weeks total including prep).
Week 1:
- Day 1: 60–90 min Audit call (map workflow, identify quick wins, estimate ROI)
- Day 2–3: You provide docs/API credentials
- Day 4–5: We scope and send blueprint
Weeks 2–3:
- Sprint builds agent (daily standups, you can test)
- Integrations tested (CRM, calendar, APIs)
- Monitoring + logging set up
Week 4:
- Deploy to production
- Monitor for 1 week
- Handover docs + ongoing support
Workflow description, API credentials, sample data, and success metrics—nothing complex.
Minimum requirements:
- Workflow description: "Here's what the sales team does manually"
- Data sources: API keys or database credentials (encrypted)
- Sample data: 3–5 examples of typical inputs/outputs
- Success metrics: "We win if X leads qualify per week"
- Tools/integrations: "Agent must log in Salesforce + check Google Calendar"
We don't need: Custom code, engineering on your end, deep AI knowledge, extensive documentation.
We deploy live, monitor for stability, gather data, calculate ROI, then you decide to scale or pivot.
Post-Sprint (Weeks 4–6):
- Deploy → Agent goes live
- Monitor → We track calls, errors, success rate
- Gather data → Real metrics (leads qualified, hours saved, $$ value)
- ROI report → Here's what it cost vs. what it generated
Your choice: Scale (expand to new workflows)? Pivot (different workflow)? Pause (no penalty)?
Typical outcome: 70% of pilots scale immediately (ROI is obvious). Some need 1–2 iterations.
Yes—we provide docs, dashboard walkthroughs, and 30-day ongoing support. Your team learns to operate it independently.
Included in Sprint:
- 2–3 hour handover training (your team)
- Written runbooks + troubleshooting guide
- 30-minute dashboard walkthrough
- Slack channel for questions (30 days free)
After 30 days: Transition to Scale partnership (monthly support) or operate independently (you keep the agent).
Goal: Make you self-sufficient so you're not dependent on us forever.
We iterate free during Sprint. If ROI doesn't materialize, we pivot the workflow or build something different.
During 14-day Sprint:
- Agent not accurate enough? Retrain on better data
- Missing integrations? We add them
- Wrong workflow? We pivot to high-value alternative
- Cost: Zero additional charge (included in Pilot)
After deployment: If ROI doesn't appear in first month, we diagnose (wrong workflow? under-trained? guardrail issue?) and fix it.
Our incentive: Your success = our repeat business.
Pricing & ROI
Audit (free) + Pilot ($8–15K) + Scale ($3–8K/month); pricing depends on complexity and volume.
Breakdown:
- Audit = Free (60–90 min discovery)
- Pilot Sprint = $8–15K (one production agent, 10–14 days)
- Scale Partnership = $3–8K/month (expand workflows, ongoing optimization)
Factors that change price:
- Complexity: Simple lead capture ($8K) vs. multi-step sales workflow ($15K)
- Integrations: Each new CRM/API integration adds ~$1–2K
- Volume: 10 calls/day vs. 1,000 calls/day affects infrastructure costs
ROI math: If a sales agent qualifies 100 leads/week at $50 value each = $5K/week value. Pilot pays back in 3 weeks.
$8K for simple single-workflow agent (lead capture, form processing, basic scheduling).
$8K agents typically handle:
- Inbound lead qualification (3–5 questions)
- Email/Slack notifications
- Calendar booking (Google Calendar only)
- Basic CRM logging
What costs extra: Multiple integrations (+$1–2K each), complex reasoning (+$2–3K), custom training on proprietary data (+$2K), multi-language support (+$1K).
Real example: Startup customer: "Qualify form submissions for $8K" → Agent reads form → checks 3 criteria → marks as qualified/unqualified → logs in Notion. Done.
$35K+ for multi-agent orchestration (5+ agents, 10+ integrations, complex RAG).
Most expensive case (IL Faro):
- Lead intake + qualification agent
- Calendar + viewing scheduler agent
- Document generation agent
- Tenant communication agent
- Finance agent (EMI tracking, payments)
Integrated with: Salesforce, Google Calendar, Google Drive, Stripe, Email + SMS + WhatsApp.
Cost: $25K pilot + $6K/month Scale = very complex, high-value workflow.
Your cost: Depends on scope. Start simple ($8K), expand (each agent ~$5–8K).
We calculate based on hours saved + revenue impact. ROI is not guaranteed, but we iterate until it appears.
ROI calculation formula:
ROI = (Hours saved/month × hourly rate) + (Revenue generated/month) - Monthly agent cost
Example: Sales agent qualifies 200 leads/month at $50 each = $10K value. Cost: $3K/month. ROI: 233% monthly.
Is ROI guaranteed? Not legally (depends on your workflow, data quality, team execution). Our approach: If ROI doesn't appear in month 1 → diagnose + iterate. If unfixable → honest conversation + consider refund.
No—Audit is free. Demos are free (on /proof). Testing during Sprint is included in Pilot cost.
Free:
- Audit call (60–90 min)
- Demo videos on /proof
- Pre-sprint consultation
Included in Pilot ($8–15K):
- 14 days of build + testing
- Live testing in staging environment
- 5–7 iterations (refinement loops)
- Monitoring setup
Not free: Custom integrations beyond scope (add-on cost), post-deployment support beyond 30 days (Scale partnership).
Security, Compliance & Data
Role-based access, audit logs, human-in-the-loop approvals, encrypted integrations—zero data exposure.
M AI security layers:
- Human approval gates — Critical actions require human sign-off
- Role-based access — Agents only access data they're authorized for
- Encrypted integrations — Credentials stored in secure vaults (not logs)
- Audit trails — Every action logged: timestamp, actor, action, result
- Data isolation — Your data never leaves your cloud region
Compliance: GDPR compliant, SOC2 ready, HIPAA compatible, NITI Aayog AI guidelines (India-specific).
Your data stays with you: We don't train on your data. We don't sell insights. We monitor outcomes only.
Guardrails prevent it; errors are logged and escalated; you have full audit trail.
Prevention layers:
- Low-risk actions (send email, update field) → Execute + log
- Medium-risk actions (delete record, process refund) → Human approval before execution
- High-risk actions (financial transactions, GDPR deletions) → Blocked by default; require explicit override
If something goes wrong: Logged instantly, escalated to human, reversible (we restore from backup), your team decides next step.
If you're in India, your data stays in India (GCP-India). GDPR subjects' data stays in EU. Full control over region.
M AI infrastructure:
- India operations: GCP-India region (Mumbai)
- EU customers: GCP-Europe region (Frankfurt)
- US customers: GCP-US region (default)
Why it matters: RBI compliance (financial data must be stored in India), GDPR (personal data must respect regional storage requirements), data sovereignty.
Your choice: You pick the region. We build agents accordingly. Zero cost difference.
Yes—we can build custom connectors for any system with an API or database access.
Systems we've connected: Oracle ERP, SAP, Salesforce, HubSpot, mainframe systems (via API wrapper), custom proprietary software, on-premise databases.
How we handle secure systems:
- You provide API credentials (encrypted in our vault)
- We build a secure connector (no credentials in code)
- Agents access via connector (not directly)
- Audit logs show every access
Timeline: Custom connector = 2–3 days additional build time, +$2–3K cost.
Yes—GDPR (EU), HIPAA (healthcare ready), SOC2 (audit-ready), India RBI guidelines.
Current compliance certifications:
✓ GDPR compliant (right to be forgotten, data portability, privacy)
✓ SOC2 Type II ready (audit-friendly, logging, access control)
✓ HIPAA compatible (can support healthcare workflows with extra guardrails)
✓ NITI Aayog AI guidelines (India-specific responsible AI)
For other regulations: We can adapt. Tell us what you need (e.g., California CCPA, Canada PIPEDA), and we'll design accordingly.
Yes—with additional guardrails, encryption, approval gates, and compliance checkpoints.
Financial workflow example: Agent processes loan applications, pulls data from secure database (encrypted), validates against compliance rules, any decision > $50K requires human approval, full audit trail for regulators.
Medical workflow example: Agent routes patient inquiries, retrieves medical history (HIPAA-compliant), never makes clinical decisions (only triage), all actions logged for compliance audits.
Extra cost: +$3–5K for compliance-grade guardrails + auditing.
Support, Maintenance & Scaling
Yes—Scale partnership ($3–8K/month) includes 24/7 monitoring, monthly updates, new workflows, and optimization.
After Sprint pilot, you have 2 choices:
Option 1: Scale Partnership (Recommended)
- $3–8K/month (depends on complexity)
- Includes: 24/7 monitoring, monthly roadmap calls, new workflows added monthly, performance optimization, model upgrades (free), Slack support channel, quarterly reviews
Option 2: Independence
- We hand over all code + docs
- You run it yourself
- Optional support: $500/month for questions
Most clients: Start Pilot → Scale partnership (compounds ROI over time).
Monthly reviews; we upgrade automatically when new models prove 30%+ better in production.
Our update cadence:
- Weekly: Test new models, frameworks, optimizations
- Monthly: Review performance, plan upgrades
- Quarterly: Major upgrades (new model, new framework)
When we upgrade: New Claude model? Test it. If 20%+ better in your use case → upgrade (free). New LangGraph release? Test. If more reliable → upgrade (free).
Your benefit: Your agents get better over time without you doing anything (and without extra cost). You can opt out of updates if you want stability.
Yes—each new workflow is $5–8K pilot + included in Scale partnership if you're already a client.
If you're on Scale partnership:
- You want to add a new agent? Let's build it
- Cost: Included in your monthly retainer (up to 3 new workflows/year)
- Timeline: 10–14 day sprint per workflow
- Benefit: We leverage existing integrations + learnings
Example: Month 1: Sales agent ($12K). Month 3: Add support agent (included in Scale). Month 6: Add content agent (included). Month 12: You have 4 agents, growing ROI.
Pilot has no lock-in (you own the agent). Scale partnerships can pause (30-day notice) with no penalty.
Pilot Sprint: 14 days, then you own the agent. No ongoing commitment.
Scale Partnership: Month-to-month (no long-term lock), pause anytime (30-day notice), no early termination fees, agents stay operational while paused.
Independence: We hand over all code, docs, and access. You maintain + run the agent. Optional support: $500/month.
Our philosophy: If you're not getting value, you shouldn't pay us. We only win if you're happy.
Guardrails, human approval, testing + monitoring—agents are designed to fail gracefully.
How we prevent hallucinations:
- Prompt engineering — Clear instructions, few-shot examples, structured outputs
- RAG (retrieval-augmented generation) — Agent answers from your docs, not imagination
- Validation layers — Agent output validated before action
- Human approval — Critical actions require human review
If agent hallucinates anyway: Caught immediately (validation rejects), logged, escalated to human, learned from (we retrain to prevent recurrence).
Comparisons & Alternatives
Agencies deploy fast & cheap; engineers build custom + long-term moats. Often, you need both.
M AI (Agency): 4–6 weeks deployment, $8–15K upfront, $3–8K/month scaling, easy to add/change workflows, we handle upgrades.
In-House Engineer: 3–6 months (hiring + onboarding), $100K+ annual, linear scaling cost, rewrites take weeks, you own everything.
Best for agencies: Fast automation, proven use cases.
Best for engineers: Custom software, competitive moat.
Truth: Many companies do both. Hire M AI for automation, engineer for product.
We specialize in agentic systems + voice agents; we charge by outcome not hours; we stay embedded post-launch.
Other agencies: "We'll build anything" (generalists), charge by hours (incentivized to slow down), fire-and-forget (project ends), vague timelines ("5–8 weeks").
M AI: Specialists in agentic AI + voice agents, charge by pilot + outcome (incentivized to ship fast), stay embedded (Scale partnership), transparent timeline ("10–14 days or we refund").
Proof: Check our case studies (ADW Finance, IL Faro) → see what we shipped.
No-code is great for simple workflows; agents need AI reasoning + tool use (beyond no-code scope).
No-code platforms excel at: If X → then Y automation, connecting APIs without code, scheduled tasks + webhooks.
No-code struggles with: Multi-step reasoning, dynamic tool selection, handling exceptions gracefully, voice/natural language workflows.
Hybrid approach (smart): M AI builds agentic logic (decision-making), n8n handles execution workflows (actions). Best of both worlds.
Real example: You use n8n today. We build an agent that uses n8n as a tool ("Execute this workflow"). Agent reasons → n8n executes.
Track Record & Trust
Yes—ADW Finance (loan operations) and IL Faro (property management). Both live, production systems.
Case Study 1: ADW Finance
- System: Loan Operations System
- Features: Loan tracking, EMI notifications, PDF generation, portfolio dashboard
- Impact: Automated loan management for 1000+ customers
- Status: Live in production
Case Study 2: IL Faro
- System: Property Management System (PMS)
- Features: Multi-property calendar, team management, booking integration, agent layer
- Impact: Manages 50+ properties, fully automated workflows
- Status: Live in production
Yes—available on request. We're not in the "review site business," but happy clients talk to prospects directly.
Why limited public reviews: Our clients are B2B (confidential workflows, competitive advantage), we focus on outcomes (not review rankings), testimonials are case-specific.
How to get testimonials: Request during audit call (we'll introduce you to similar clients), LinkedIn recommendations (ask our team), client success stories (custom to your use case).
Our guarantee: If we say we can build something, we can. If you're not happy → iterate or refund.
Yes to both. We're bootstrapped (not burning VC money), growing profitably, and committed to long-term client partnerships.
M AI financials: Founded 2024, profitable since month 3, 40+ agent deployments (as of Jan 2026), team of 15+ engineers + AI specialists.
Why we're stable: Not burning investor money (no pressure to fail fast), recurring revenue model (Scale partnerships), happy clients stay (95%+ retention), growing slower = sustainable.
Your risk: Zero. If we disappear (unlikely), you own all code + infrastructure.
We're here. Scale partnerships are month-to-month (no "end date"). You can pause/resume anytime.
Support commitments: Pilot (30 days post-launch included), Scale partnership (ongoing, month-to-month), after you leave (your agents keep running, we hand over code).
3-year timeline: Year 1 (scale from 1 to 5 agents), Year 2 (optimize, reduce costs, grow ROI), Year 3 (self-sufficient or still partnering for new workflows).
Our commitment: We're here as long as you need us. Pause anytime. Resume anytime. No penalties.
Technical Deep-Dives
RAG = agent answers from your documents/data (not imagination). Use it when agents need to reference knowledge bases.
How RAG works:
- You upload docs (manuals, policies, FAQs, contracts)
- Agent searches docs for relevant info
- Agent answers with citations ("See page 5 of handbook")
- No hallucination (answer is grounded in reality)
When you need RAG: Customer support, HR policies, legal queries, product questions.
When you don't need RAG: Sales qualification (facts in CRM), lead enrichment (data from APIs), workflow execution.
Cost: RAG adds ~$2K to pilot, included in Scale.
OpenAI/Anthropic build models; we build complete systems (models + guardrails + integrations + monitoring + support).
OpenAI Assistants: You get playground to test prompts. You miss production infrastructure, monitoring, integrations, handoff.
Anthropic tools/MCP: You get tool protocol specification. You miss implementation, orchestration, monitoring, support.
M AI: You get production agent, integrations, monitoring, guardrails, human-in-the-loop, ongoing support. We use OpenAI/Anthropic models (the best ones).
Analogy: OpenAI/Anthropic = car engine. M AI = complete car + fuel + insurance + support.
Yes—we customize prompts, response templates, and brand voice. Your agent sounds like your brand.
Customization options: Tone (friendly, formal, playful), language (brand-specific vocabulary), responses (custom templates), guardrails (personality-aware).
Example: Property agent (IL Faro): Professional + warm (not salesy). Sales agent (ADW Finance): Confident + helpful (not pushy).
How we do it: Fine-tune prompts based on your brand guidelines, test with 10+ scenarios, iterate until it matches your voice.
Cost: Included in pilot.
We monitor continuously. If performance drops, we diagnose + retrain. Included in Scale partnership.
How drift happens: Input data changes, model behavior changes (subtle LLM updates), integration changes (third-party API changes).
How we prevent it:
- Continuous monitoring — Track accuracy, success rate, user satisfaction
- Monthly reviews — Spot trends early
- Automated alerts — Drop below threshold? We investigate
- Proactive retraining — Fine-tune on fresh data quarterly
Cost: Included in Scale partnership.
Yes—with human feedback. Agents don't auto-improve (too risky), but we retrain monthly on reviewed interactions.
How learning works: Agent makes decisions, you provide feedback (thumbs up/down, or override), we review interactions monthly, we retrain agent on feedback data, agent gets better.
Real example: Sales agent qualifies leads. Month 1: Misses 30% of opportunities. You mark them. Month 2: We retrain. Agent now catches 90% of opportunities.
Why we don't auto-improve: Too risky. Agent might learn bad patterns. Human oversight is safer.
Cost: Included in Scale.
You own all code + infrastructure. Agents keep running independently. Migration is easy (your choice, your timeline).
Your code ownership: All agent code = yours, all integrations = documented, all data = in your systems, all logs = yours to keep.
If M AI disappears: Your agents still run (serverless infrastructure), you can hire any engineer to maintain them, no vendor lock-in.
If you want to switch providers: We hand over all code, we provide documentation, we introduce you to successor (if helpful). Timeline: Your choice.
Our commitment: We're not a black box. You can leave whenever you want.
More Questions?
All information provided is for general guidance only. See our legal disclaimer for important terms and limitations.