AI Agents & Agentic Workflows
Purpose-built AI agents that autonomously execute complex business workflows — from multi-step research and document processing to customer onboarding, lead qualification, and internal operations. Our agentic AI systems reason, plan, and act across your tools and data sources.
Why this matters
A chatbot answers one question at a time. An agent does the actual work — pulls a document from SharePoint, extracts the customer's IBAN, validates it against your bank-API rules, books the next step in your CRM, and tells the human only when it hits something it can't decide. The hard part isn't the model — it's wiring a reliable loop with retries, guardrails, audit trails, and a clear stop condition that doesn't accidentally email 5,000 customers because the prompt drifted. Mantiqi builds agents that survive contact with production: scoped tool access, evaluation harnesses, kill switches, and human review on the steps that matter.
What We Deliver
How we deliver
- 01
Workflow decomposition
Week 1We pick the candidate workflow (onboarding a customer, processing a refund, qualifying a lead, extracting fields from invoices) and break it down into discrete steps — what data, which tools, what's safe for the agent to do alone, what needs human review.
- Step-by-step workflow map
- Tool / API inventory the agent needs
- Human-review checkpoints
- Kill-switch + escalation rules
- 02
Agent + tool wiring
Weeks 2–4We build the agent loop, connect each tool with proper auth + rate-limiting, add guardrails (PII redaction, off-scope refusal, max-steps caps), and ship an internal eval harness that grades a held-out set of representative tasks every commit.
- Agent loop with tool definitions
- Auth + rate-limiting per tool
- Eval harness with grading criteria
- Observability + step-by-step traces
- 03
Pilot + supervised launch
Weeks 5–7Agent runs in shadow mode first — execution is recorded but a human approves each step. Once shadow accuracy passes the bar (typically 95%+ on the eval set), we promote to supervised live, then to autonomous on the steps where the eval supports it.
- Shadow-mode pilot with human approval
- Promotion criteria documented
- Per-step autonomy graduation
- Production launch + monitoring
- 04
Operate + extend
OngoingDaily eval-set runs catch regressions before users do. New tools and workflows added incrementally as the team finds new uses. Cost dashboards show per-agent-call spend so the unit economics stay sane.
- Daily eval reports
- Cost-per-call dashboard
- Quarterly capability expansions
- Optional managed-operations tier
Frequently asked questions
Chatbots respond — agents act. A chatbot answers "What's our refund policy?". An agent processes the actual refund: validates the order in your CRM, checks the date against the policy, issues the credit through your payment processor, updates the ticket, and emails the customer — all autonomously, with human review on the steps that need it. Same model family underneath; different surface and safety architecture.
Still have questions?
Our team is ready to help. Reach out and we'll get back to you as soon as possible.
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Free agent feasibility scoring
Tell us about a workflow you'd want to automate — what your team does today, how often, where the bottleneck is. We'll send back a written feasibility score covering build effort, expected accuracy, blast-radius / safety considerations, and a rough monthly run-cost.
Agent builds typically scope at SAR 80,000–250,000 for a first production agent (workflow decomposition + build + eval harness + supervised launch). Per-call inference cost varies by complexity — typically SAR 0.10–2.00 per agent run. Managed-operations tier from SAR 8,000 / mo.
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