AI That Thinks.
AI That Acts.
Multi-agent systems that plan, execute, and self-correct across your entire operation. No human babysitting. No brittle scripts. Just autonomous workflows that compound in value every week they run.
Built on n8n, OpenAI, Claude, and Gemini with custom orchestration logic that turns disconnected tools into a unified, intelligent system.
What Is Agentic AI & How Is It Different?
Most automation tools follow scripts. They do exactly what you tell them, and nothing more. When something unexpected happens, they break. Agentic AI is fundamentally different because it operates with autonomy, reasoning, and self-correction.
An agentic system decomposes a goal into subtasks, selects the right tools to accomplish each step, monitors its own progress, and adjusts its strategy when outcomes deviate from expectations. It is the difference between giving someone a to-do list and hiring someone who figures out the to-do list on their own.
| Dimension | Agentic AI | RPA | Workflow Automation | Chatbots |
|---|---|---|---|---|
| Decision Making | Autonomous reasoning with chain-of-thought planning | Rule-based, follows exact scripts | Conditional branching on predefined paths | Single-turn intent matching |
| Error Handling | Self-corrects, retries with alternative strategies | Halts or fails silently | Follows error branch if configured | Escalates to human |
| Adaptability | Learns from feedback, adjusts behavior over time | Zero adaptability without re-scripting | Limited to pre-built logic | Re-training required for new intents |
| Complexity Ceiling | Multi-step, multi-tool, cross-system orchestration | Single-application screen automation | Moderate multi-step pipelines | Simple Q&A and form fills |
Multi-Agent Architecture in Practice
In a multi-agent system, no single AI does everything. Instead, specialized agents collaborate under an orchestrator. One agent handles data extraction, another performs analysis, a third generates outputs, and a fourth validates quality. They communicate through structured protocols, share context through a centralized state layer, and escalate to humans only when confidence drops below configurable thresholds. The result is a system that handles complexity no single automation tool could manage alone.
Our Agentic AI Architecture
Every system we build follows a four-layer architecture designed for reliability, observability, and incremental complexity. We do not believe in black-box AI. Every decision is traceable, every action is logged, and every agent is independently testable.
Layer 01
Orchestrator Layer
n8n serves as the central nervous system, routing tasks to specialist agents, managing state, and coordinating multi-step execution flows across your entire stack.
Layer 02
Reasoning Engine
Large language models handle planning, analysis, and decision-making. We select the optimal model per task based on cost, latency, and accuracy requirements.
Layer 03
Specialist Agents
Purpose-built agents with scoped permissions and domain-specific prompts. Each agent owns a single responsibility and communicates through the orchestrator.
Layer 04
State & Memory
Persistent state management ensures agents maintain context across sessions, track progress on multi-step tasks, and avoid redundant operations.
End-to-End Agent Execution Flow
Trigger
Webhook / Schedule / Event
Orchestrator
n8n Routes to Agents
Reasoning
LLM Plans & Decides
Tool Execution
APIs, DBs, Services
Validation
Quality Check & Retry
Output
Action, Report, or Alert
Agentic AI in the Real World
These are not hypothetical scenarios. Each use case is drawn from production deployments for real businesses with real constraints and real measurable outcomes.
Use Case 01
Self-Healing CRM
Data Quality, Deduplication & Enrichment
The Challenge
A B2B SaaS company was losing deals because their CRM contained 40% duplicate records, stale contact data, and inconsistent field formatting. Sales reps spent 6+ hours per week on manual data cleanup instead of selling.
The Solution
We deployed a multi-agent system with three specialist agents: a Data Auditor that continuously scans for duplicates and inconsistencies, an Enrichment Agent that pulls real-time firmographic and contact data from Clearbit and LinkedIn, and a Reconciliation Agent that merges records using fuzzy matching with human-in-the-loop approval for edge cases.
Results
Use Case 02
Intelligent Lead Routing
Scoring, Assignment & Follow-Up Sequencing
The Challenge
A performance marketing consultancy was routing leads manually via Slack messages. Average response time was 4.2 hours, with 23% of qualified leads falling through the cracks entirely. Their scoring model was a spreadsheet that nobody trusted.
The Solution
We built an agentic pipeline that ingests leads from six channels, runs them through an AI scoring agent trained on 18 months of conversion data, routes to the optimal rep based on capacity, expertise, and timezone, then orchestrates a personalized follow-up sequence with dynamic content generated per lead.
Results
Use Case 03
Autonomous Content Operations
Research, Draft, Review & Publish
The Challenge
A creator economy agency was producing 8 pieces of content per week with a team of 4. Every piece required manual research, drafting, internal review, revision cycles, and cross-platform publishing. Bottleneck was always the review stage.
The Solution
We deployed a four-agent content pipeline: a Research Agent that monitors industry trends and compiles briefs, a Drafting Agent that generates platform-optimized content, a Review Agent that evaluates against brand guidelines and SEO benchmarks, and a Publishing Agent that schedules and distributes across 5 platforms with per-channel formatting.
Results
Use Case 04
Financial Ops Automation
Invoice Processing, Reconciliation & Anomaly Detection
The Challenge
A global staffing marketplace processed 2,000+ invoices monthly across 14 currencies. Reconciliation was a 3-person, 5-day-per-month operation with a 4.7% error rate that had already caused two compliance incidents.
The Solution
We built an agentic financial operations system with an Extraction Agent that parses invoices from email, PDF, and portal sources using vision models, a Reconciliation Agent that matches line items against purchase orders and flags discrepancies, and an Anomaly Detection Agent that identifies unusual patterns, duplicate charges, and currency conversion errors in real time.
Results
From Discovery to Production in 5 Phases
Every engagement follows a structured path from audit to deployment. No phase begins until the previous one delivers a concrete artifact. You always know exactly where you are and what comes next.
1 week
$2,500 - $4,000
Discovery & Audit
We map your current workflows, identify automation-ready processes, evaluate data infrastructure, and define the highest-ROI use case for your first agentic deployment.
2 weeks
Included in MVP build
Agent Architecture Design
We design the multi-agent architecture, define agent roles and communication protocols, select optimal models per task, and plan the integration topology with your existing systems.
2 - 4 weeks
$10,000 - $20,000
MVP Build & Integration
We build the core agentic system, integrate with your tools, implement error handling and fallback logic, and deploy a functional MVP that handles your primary use case end-to-end.
1 - 2 weeks
Included in MVP build
Testing, Calibration & Human-in-the-Loop
We stress-test the system with real data, calibrate agent behavior through prompt tuning, configure human approval gates for high-stakes decisions, and validate accuracy against your quality benchmarks.
Ongoing
$3,500 - $8,000/month
Production Launch & Optimization
We launch the system into production, monitor performance metrics, optimize based on real-world feedback, and iterate on agent behavior to continuously improve accuracy and reduce costs.
Transparent Pricing
No surprise invoices. No scope creep without conversation. Every engagement starts with a defined scope, clear deliverables, and a price you agree to before work begins.
Discovery
$2,500 - $4,000
Process audit, data assessment, and architecture recommendation
MVP Build
$10,000 - $20,000
End-to-end agentic system for your primary use case
Enterprise
$25,000 - $50,000+
Complex multi-agent deployments with custom model training
Retainer
$3,500 - $8,000/mo
Ongoing optimization, monitoring, and expansion of your agentic systems
Frequently Asked Questions
Regular automation follows predefined rules: if X happens, do Y. Agentic AI reasons about problems, plans multi-step solutions, executes across tools, and self-corrects when things go wrong. Think of it as the difference between a calculator and an analyst. A calculator does what you tell it. An analyst understands the problem, decides what to calculate, checks if the answer makes sense, and adjusts the approach if it does not.
A typical project runs 5 to 8 weeks from discovery to production launch. The discovery phase takes 1 week, architecture design takes 2 weeks (often overlapping with build), MVP build takes 2 to 4 weeks, and testing and calibration takes 1 to 2 weeks. Enterprise deployments with complex integrations or custom model training may extend to 12 weeks. We always start with a focused MVP and expand from there.
Every agentic system we deploy includes human-in-the-loop checkpoints for high-stakes decisions. Agents flag low-confidence outputs for human review, and all actions are logged with full audit trails. We also implement rollback mechanisms so any agent action can be reversed. In practice, our systems typically achieve higher accuracy than manual processes within the first month of deployment.
In most cases, no. We use pre-trained foundation models (GPT-4, Claude, Gemini) that already have broad capabilities. What we do need is access to your existing systems, documentation of your current processes, and examples of good outcomes. For specialized use cases, we may fine-tune models on your historical data, but this is usually a Phase 2 optimization rather than a launch requirement.
Yes. Our orchestration layer (n8n) supports 400+ native integrations, and we build custom API connectors for anything not covered out of the box. We have deployed agentic systems that integrate with Salesforce, HubSpot, Slack, Airtable, Google Workspace, Jira, Asana, QuickBooks, Stripe, custom ERPs, and proprietary internal tools. If it has an API, we can connect it.
Most clients see positive ROI within 30 to 60 days of production launch. The primary value drivers are labor hours saved (typically 10 to 15 hours per employee per week for targeted processes), error reduction (80 to 95% reduction in data quality issues), and speed improvements (response times measured in seconds instead of hours). We provide a detailed ROI projection during the discovery phase before you commit to a build.
Yes. Our Discovery phase functions as a paid proof-of-concept. For $2,500 to $4,000, you get a complete process audit, architecture recommendation, and ROI projection. If we determine that agentic AI is not the right fit for your use case, we will tell you and recommend a simpler approach. We would rather lose a project than deploy something that does not deliver value.
After launch, we offer retainer packages ($3,500 to $8,000 per month) that include ongoing performance monitoring, monthly optimization cycles, prompt refinement based on real-world feedback, and development of new agents as your needs evolve. Most clients start with a single use case and expand to 3 to 5 agentic systems within the first year. We also provide documentation and training so your team can handle day-to-day oversight.
Related Services & Work
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