What are AI agents and how are they different from chatbots?

AI agents are autonomous software systems capable of reasoning, planning, and taking action across digital environments with minimal human direction. Unlike off-the-shelf AI tools and chatbots, which follow predefined scripts and respond to queries, intelligent AI agents execute multi-step tasks, use tools, interact with external systems, and make decisions based on context. A chatbot answers a question. An AI agent can research, coordinate across systems, act on the user's behalf, and complete a workflow end-to-end. That distinction determines what level of automation is actually achievable for your business.

What is a multi-agent system and when does a business need one?

A multi-agent system is a coordinated network of specialized AI agents, each operating within a defined role and sharing context to solve problems no single agent could handle alone. A business needs one when a workflow is too complex or multi-disciplinary for a single agent. This is common when automating end-to-end HR or finance processes involving multiple data sources, tools, and access rules. Multi-agent systems are also the right architecture when different departments or user roles need different agent capabilities and permissions under a single unified platform.

What business processes can AI agents automate?

AI agents are best suited to knowledge-intensive, multi-step workflows where context, reasoning, and cross-system action are required. These are precisely the processes that rule-based automation and chatbots cannot reach. Common applications include HR policy Q&A and employee data management, financial reporting and analysis, customer support resolution, legal document review, onboarding automation, IT operations, and research workflows. Any process that currently requires a human to gather information from multiple sources, apply judgment, and take action across systems is a strong candidate for agentic AI automation.

How secure are enterprise AI agents and multi-agent systems?

Enterprise AI agents are built with security and compliance as architectural requirements, not afterthoughts. Properly designed agentic AI systems implement role-based access controls, audit trails, encryption at rest and in transit, and human-in-the-loop checkpoints that define what agents handle autonomously versus what requires human approval. For multi-agent systems, security is applied at both individual agent and system-wide levels. AI observability pipelines and automated evaluations continuously monitor agent behavior, detect drift, and maintain accuracy standards throughout the full production lifecycle.

How long does it take to develop and deploy an AI agentic solution?

Timelines depend on complexity. A single-purpose AI agent, such as a knowledge retrieval assistant or workflow automation agent, typically reaches a working prototype within four to eight weeks and full production deployment within two to four months. Multi-agent systems with broader orchestration, tool integrations, and enterprise infrastructure typically take three to six months. In both cases, agent reasoning and workflow execution are validated against real data early, reducing risk before full-scale build begins. The goal is a working system in production, not an extended pilot.

Can AI agents integrate with existing enterprise systems?

Yes, integration with existing enterprise systems is central to how AI agents deliver value. AI agents connect to CRMs, ERPs, SharePoint, ticketing systems, project management platforms, internal knowledge bases, databases, and custom APIs through secure function calling, MCP servers, and API layers. Rather than replacing your existing infrastructure, agentic AI is designed to operate within it, accessing real-time data and executing actions across the tools your teams already use. Integration requirements are assessed during the discovery phase, with architectures designed to minimize disruption to current operations.

Vibe Code to Production: Codebase Audit
and Readiness Check

Built with AI? Start with a 3-day check. Go deeper only if it’s worth it.

State Analysis

3 days

Is your vibe code secure and well built?

You get

a readiness score
ranked risks
a go/no-go call

Deep Assessment

~7 days

What will it take to reach production?

You get

reconstructed documentation
a production roadmap
a stakeholder briefing

CHECK YOUR VIBE CODE

Clients

What You Get From the Assessment

Six components that take your vibe-coded product from a working prototype to a
phased roadmap for production.

Scan every file in the codebase, not a representative sample

Static analysis, dependency mapping, security and secrets scanning, and test coverage measurement run across your entire codebase rather than a representative sample. Agentic AI code reading covers every file, so nothing is skipped for time. You get a factual inventory of what is there: structure, libraries, exposed credentials, technical-debt signals, and the gap between what the product claims to do and what the code actually does.

01

Automated Whole-Codebase Analysis

Scan every file in the codebase, not a representative sample

Static analysis, dependency mapping, security and secrets scanning, and test coverage measurement run across your entire codebase rather than a representative sample. Agentic AI code reading covers every file, so nothing is skipped for time. You get a factual inventory of what is there: structure, libraries, exposed credentials, technical-debt signals, and the gap between what the product claims to do and what the code actually does.

02

Solution Architect Expert Review

Turn scan output into judgment a business decision can rest on

Automated tooling can flag a hundred problems. A solution architect tells you which three matter before launch. Our architects don’t rely on tooling alone. They also review the code manually and bring their own experience of how a product like yours should be built. They weigh the scan output against your business context, separate genuine risk from scanner noise, assess whether the architecture can carry the product you intend to build, and commit to a recommendation. Two short sessions, one with you and one technical, keep the findings anchored to what you are actually trying to ship.

Deliverables: a written recommendation, including the go/no-go call.

03

Seven-Dimension Readiness Scorecard

Score the codebase on the same seven dimensions, every single time

Every codebase is scored against the same seven dimensions: security and secrets, data integrity and model, functional correctness, architectural coherence, test and observability coverage, scalability headroom, and maintainability. Consistent scoring means your result is comparable rather than anecdotal, repeatable across future releases, and defensible when an investor or board asks how you know. You receive a readiness level and a risk heatmap that ranks what to fix first.

Deliverables: readiness scorecard, readiness level, risk heatmap.

04

Architecture and Documentation Reconstruction

Document the architecture and data model nobody has written down yet

AI-built products often have little documentation, and the person who prompted them into existence cannot always explain how they work. We reconstruct the architecture, data model, and key flows, documenting them for the first time. That pack shows how the system is actually put together and where it is fragile, so future development decisions stop depending on one person’s memory of a chat session.

Deliverables: architecture pack, documentation pack.

05

Roadmap to Production

Turn the findings into a phased plan your team can execute

A report tells you what is wrong. A roadmap tells you what to do about it. Findings are translated into a phased, sequenced plan across four stages: stabilize, secure and observe, scale and maintain, then productize. Each phase is prioritized, with quick wins flagged, so you can decide what to act on now and what can wait, whether your own team executes it or we do.

Deliverables: production roadmap, stakeholder briefing.

06

AI Development Toolkit and Guardrails

Leave with a configured toolkit for safer AI-assisted building

You keep a configured AI toolkit whether or not the engagement continues. We select the right agentic frameworks for your stack, write proper project instructions, and set guardrails so your next round of AI-assisted building starts structured instead of improvised. The assessment itself is stack agnostic, with in-house depth in .NET, Node, and Python, and more, so bring the codebase exactly as it stands today.

Deliverables: configured AI toolkit and project guardrails.

Start Your Codebase Audit

Case Study

From Zero Cloud Infrastructure to a Production AWS Environment

An aging-care coordination platform went from a prototype with no cloud infrastructure to a QA-passed, cutover-ready MVP in about four months, plus an optional month of polishing, with 100% automated releases, 14 live production AWS services, and no in-house DevOps hires.

View Case Study

Case Study

Building a ‘Second Brain’ AI Platform for Efficient Development Teams

A multi-agent AI platform that gives development teams organizational memory across Jira, GitHub, Confluence, and email while boosting sprint velocity by 50%, cutting meeting time by 52%, and achieving 99.5% accuracy in automated ticket generation.

View Case Study

AI Proof of Concept (PoC) Development Services

Case Study

Multi-Agent Automation for Everyday Corporate Workflows

Symphony Buddy, a multi-agent AI platform in Microsoft Teams, routes employee queries to role-specific agents for HR policy Q&A, ERP data lookups, and People Partner workflows – built and live in under 3 months.

View Case Study

Case Study

AI-Driven Assistant Transforms Betting & Casino Experience

BetHarmony, a smart AI assistant, redefines iGaming by offering a seamless, personalized betting journey that captivates and engages at every turn.

View Case Study

Case Study

Scaling Vivino into the World’s Largest Online Wine Marketplace

See how we helped Vivino scale from a small startup into the world’s largest online wine marketplace and most downloaded wine app, with 60M+ users and over 1B label scans, by designing and operating a cloud-native architecture built for massive data growth and peak loads.

View Case Study

Tell us about your project

What Changes After the Assessment

The assessment shows what your vibe-coded product needs to launch and whether it’s worth the
investment. The value builds from week one to a maintainable production system.

Immediate Impact (1–3 Months)

Clear Go/No-Go Decision: A scored, evidence-based read on your codebase in three days, fast enough to inform a launch or funding decision this quarter.

Ranked Risk Register: Security gaps and data exposure ranked by severity, so the most dangerous issues are fixed first.

Fixed Timeline, Known Scope: A fixed State Analysis with a defined scope, so you know what finding out involves before you commit to anything further.

Medium-Term Impact (3–6 Months)

Reconstructed Documentation: Architecture, data model, and key flows written down for the first time, so the product stops living in one person’s head.

Roadmap Execution Underway: Stabilization and security hardening executed against a prioritized plan rather than reacted to as problems surface in production.

Technical Validation: A clear view of whether the codebase has potential for further development and what it will take to build forward, before you spend on any of it.

Long-Term Impact (6–18 Months)

Production-Grade System: Once the roadmap is executed, scalability bottlenecks are removed and the codebase is brought to a maintainable baseline that new engineers can join and extend.

Repeatable Readiness Process: The same seven-dimension scorecard reused across future releases and new AI-built prototypes.

AI-Assisted Development at Scale: A configured AI toolkit and guardrails let your team keep building with AI without accumulating the same risks again.

Evaluate Your Production Readiness

How Do We Work on AI-Built Products?

Two tiers with a decision point between them. Start with the fixed three-day check, go deeper only if it earns it.

Tier 1

State Analysis · 3 days

Find out in three days whether going deeper is worth it.

Three business days, fixed. Static code analysis, a security and secrets scan, and a structure and dependency read, with no running instance required. Plus a founder session and a technical session. Staffed by a solution architect with developer assistance.

Deliverables:

readiness level scored against a risk heatmap
ranked risks and production must-haves
a recommendation on whether to go deeper

Tier 2

Deep Assessment · ~7 days

Go from a scored read to a documented path to production.

Around seven additional business days, based on State Analysis findings. We run the application, document its architecture, and assess its data model and scalability. Delivered by a part-time business analyst, architect, and developer.

Deliverables:

reconstructed documentation pack
prioritized production roadmap
recommendation, stakeholder presentation

Start with State Analysis

How to Get Started

From repository access to a scored read in three business days, with a decision
point before any deeper work begins

01

Grant read access to the repository

Read-only access to the source code is all we need to start. No running instance or environment setup is required.

02

Book the two short sessions

A one-hour founder session covering intent and context, plus a thirty-minute technical session with whoever built it.

03

Receive your readiness score

Within three business days, you get a readiness level, a ranked risk heatmap, and the must-haves for production.

04

Decide whether to go deeper

Stop here with a full picture of your codebase, or continue to the Deep Assessment, with its effort already scoped by the findings.

05

Get your production roadmap

In the Deep Assessment, we run the application, reconstruct its architecture and documentation, and hand over a phased roadmap and stakeholder briefing.

06

Move to production

Execute the roadmap with your own team, or have us stabilize, deploy, and keep building the product. Either way, you keep a configured AI toolkit for future development.

Get Your Readiness Score

Our Industry Experts

Oleksandr Volynets

Lead AI Solutions Architect

Oleh Vykhopen

Director of Technology

Kseniya Kobryn

CEO

Igor Perekhrest

АІ Team Lead

Why Work With Us on Your Vibe-Coded Project?

Bring a vibe-coded project to most firms and they will assemble a full team and quote you a full delivery program. You wanted the thing working, not a shipyard. We have worked at this scale and size the review to match it, which is why the entry point is three fixed days rather than a presale.

01

Right-sized for what you actually built

Bring a vibe-coded project to most firms and they will assemble a full team and quote you a full delivery program. You wanted the thing working, not a shipyard. We have worked at this scale and size the review to match it, which is why the entry point is three fixed days rather than a presale.

02

Engineers who fix, not auditors who certify

Compliance audits exist to issue a document confirming you meet a standard. This is not that. Think of the difference between the inspector who certifies a car as roadworthy and the mechanic who tells you what to repair and then repairs it. We are the second, and if you want the work done after the findings land, we can do it.

03

Every finding validated by a solution architect

Scanners produce volume, not judgment. A solution architect reviews each automated finding against your business context, discards false signals, and tells you which issues actually block launch. You get a ranked recommendation you can act on rather than a raw tool export you have to interpret yourself.

04

We build AI products, not just audit them

Symphony develops its own AI products, including Harmony, so we build with agentic tooling, not just review what it produces. That is why we can tell you which agentic frameworks suit your stack, write project instructions that hold up, and leave your team with guardrails that keep the next round of AI-assisted building out of the same trouble.

05

Low risk, and yours to walk away with

Small upfront investment, no obligation to continue. Take the report and deploy the product yourself if you want to. Or have us stabilize and deploy it, or build the next features too. Either way you leave with a configured AI toolkit for your own future builds.

Talk to a Solution Architect

Industries We Support

Vibe-coding tools write the same code everywhere, but its mistakes cost differently by sector: a mishandled patient record, an untraceable payment, a booking system that fails under load. Our deepest experience is in the industries below, but we audit any sector.

Healthcare

Read Our Case Studies

iGaming

Read Our Case Studies

eCommerce

Retail

Read Our Case Studies

Insurance

Marketing and Advertising

Read Our Case Studies

Travel and Hospitality

Read Our Case Studies

Education and
eLearning

Read Our Case Studies

Energy and Utilities

Supply Chain

Read Our Case Studies

FinTech

Read Our Case Studies