TechEniac’s AI agent development services go beyond chatbots and automation scripts. We build multi-agent systems where specialised AI agents collaborate on complex tasks reasoning through decisions, executing multi-step workflows, and escalating to humans at exactly the right moment. Built with LangGraph. Battle-tested in production.
Our multi-agent AI systems don’t just generate text they complete work. Process insurance claims end-to-end. Coordinate hospital operations across 800+ beds. Screen and schedule candidates autonomously. Monitor regulatory changes across 60+ sources. Six multi-agent systems in production across healthcare, fintech, recruiting, and enterprise automation more than any boutique AI agency.
Describe your complex workflow. We’ll assess whether multi-agent architecture is the right approach or whether a simpler solution would serve you better.
Trusted in production
AI Agent Development Services
Chatbots respond to queries. Automation scripts follow predefined rules. AI agents reason through complex situations, make decisions, execute actions across multiple systems, and adapt their behaviour based on outcomes. We build all three but this page is about the agents.
The shift from single-model AI to multi-agent systems is the most significant architectural evolution in AI product development. Single models hit an accuracy ceiling around 75–85% for complex domain-specific tasks. Multi-agent systems break through that ceiling by decomposing complex reasoning into specialised sub-tasks, each handled by an agent optimised for that specific operation.
TechEniac has 6 multi-agent systems in production — SolidHealth AI (5 agents), PatientFlow AI (4 agents), TalentSync AI (5 agents), ComplianceGuard AI (2+ agents), WealthPilot AI (4 agents), and WorkflowAI (decision nodes). These are production platforms serving real users and processing real decisions daily.
Our AI Agent Development Services
Six capability areas spanning multi-agent orchestration, autonomous task execution, verification systems, regulatory monitoring, workflow decision-making, and human-in-the-loop design each engineered for production reliability in complex, high-stakes domains.
Systems where multiple specialised AI agents work together on complex tasks each agent handling one responsibility within a larger coordinated workflow. Sequential pipelines, hub-and-spoke dispatch, or supervised autonomous operation. The architecture matches the problem.
SolidHealth AI uses 5 agents in a sequential pipeline to achieve 95% medical accuracy through self-correcting verification loops. PatientFlow AI uses a hub-and-spoke orchestrator dispatching 4 specialised agents based on real-time hospital state.
Agents that don't just generate text they complete work. Process insurance claims end-to-end. Coordinate hospital bed assignments across departments. Screen candidates and schedule interviews. Trigger APIs, update databases, and route decisions all autonomously within defined boundaries.
TalentSync AI's 5 agents handle the complete pre-screening workflow from job posting to qualified shortlist saving recruiters 68% of their administrative time. 50+ companies onboarded in 3 months.
Single-model AI hits an accuracy ceiling around 75–85% for complex domain tasks. We break through that ceiling by adding verification, fact-checking, and self-correction agents around the primary generation agent. The AI checks its own work before delivering results.
SolidHealth AI's self-attention feedback loop catches 23% of accuracy issues before responses reach patients pushing accuracy from ~80% to 95%+. The verification agents are the difference between a demo and a product patients can trust.
Agents that continuously scan regulatory sources, detect relevant changes, and assess impact on your business automatically. New regulations are detected within hours, not weeks. Impact assessments run per-client without manual review.
ComplianceGuard AI monitors 60+ regulatory sources and assesses impact across 35 fintech client companies with 89% assessment accuracy. New documents detected within 4 hours of publication.
AI decision-making embedded directly into enterprise workflows agents that receive execution context, make structured routing decisions, and log their reasoning to an audit trail. Lead qualification, risk scoring, ticket categorisation, and escalation triggers handled by AI, verified by humans.
WorkflowAI's decision nodes automate an average of 22 hours per week of operations team time per client across 120+ enterprise beta clients. 97.3% workflow success rate with 100% audit logging.
We never build fully autonomous agents for high-stakes domains. Every system includes human oversight mechanisms appropriate to the risk level physician confirmation, recruiter override, approver validation. The agent assists. The human decides. Always.
PatientFlow AI generates bed assignment recommendations but requires attending physician confirmation for high-acuity transfers. TalentSync AI gives recruiters one-click override on every agent decision. WorkflowAI pauses at configured approval nodes.
Industries We Serve
Our AI agent development services are tailored to the specific workflow complexity, accuracy requirements, and compliance frameworks of each industry.
Multi-agent medical verification systems and hospital operations coordination. Patient-facing health guidance with 95%+ accuracy. Multi-facility agent orchestration across 800+ beds. HIPAA-compliant with FHIR integration.
Regulatory monitoring agents scanning 60+ sources. Impact assessment automation across multiple client companies. FCA-compliant financial analysis with boundary management. Open Banking integration.
Autonomous pre-screening workflows job decomposition, resume parsing, candidate scoring, personalised outreach, and interview scheduling. Multi-model strategies (Gemini for parsing, GPT-4o for scoring).
AI decision nodes embedded in workflow platforms. Lead qualification, risk scoring, compliance checking, ticket categorisation, and automated routing with human-in-the-loop approval at configured checkpoints.
Conversational AI agents for claims intake and processing. Multi-turn data collection, policy validation, CMS population, and FCA-compliant communication record generation all within a single agent-driven workflow.
Our Development Approach
Building a multi-agent system isn’t like building a chatbot. Each agent is designed, developed, and tested individually then integrated into an orchestrated pipeline and tested again as a system. Here is how we approach every multi-agent engagement.
A complete agent architecture document defining each agent's responsibility, data requirements, tool access permissions, communication patterns, and human oversight checkpoints.
We decompose the complex task into discrete sub-tasks, each becoming a candidate agent. What does each agent do? What data does it need? What tools can it access? How do agents communicate? Where does human oversight fit? SolidHealth AI's decomposition produced 5 agents each with a single, well-defined responsibility making the system debuggable, testable, and improvable at the individual agent level.
A production-ready orchestration layer using LangGraph with explicit state management, conditional branching, and persistent context across sessions.
Three orchestration patterns cover most production use cases. Sequential Pipeline: agents execute in a fixed order, each output feeding the next (SolidHealth AI: validate → retrieve → generate → verify → format). Hub-and-Spoke: a central orchestrator dispatches specialised agents based on dynamic conditions (PatientFlow AI: orchestrator dispatches bed, surgical, discharge, and forecasting agents based on real-time hospital state). Supervised Autonomous: agents operate independently within boundaries, escalating at configured checkpoints (TalentSync AI: agents handle pre-screening end-to-end, recruiters review summary cards with one-click override).
Individually developed, tested, and validated agents each with its own system prompt, tool permissions, input/output schema, quality criteria, and the optimal LLM for its specific task.
Each agent gets the right model: GPT-4o for complex reasoning (screening scoring, impact assessment, clinical NLP). Claude Sonnet for compliance-sensitive tasks (regulatory classification, FCA boundary management). Gemini for document understanding (resume parsing, damage assessment). ComplianceGuard AI's Impact Assessment Agent was tested against 200 known regulatory changes refined until it achieved 89% accuracy independently before integration.
A fully integrated multi-agent pipeline tested for state propagation, conflicting decisions, circular dependencies, latency accumulation, and end-to-end accuracy with LangSmith observability tracing every agent call.
Individual agents working perfectly can still fail as a system. Integration testing catches issues single-agent testing cannot. LangSmith provides per-agent tracing input, output, latency, token usage, and decision rationale per step. When a pipeline produces an incorrect result, we pinpoint exactly which agent made the error reducing debugging from hours to minutes. A 5-agent pipeline where each agent takes 2 seconds = 10-second total response time. We optimise at integration to bring this within acceptable bounds.
Human oversight mechanisms appropriate to your domain's risk level configured, tested, and integrated into the agent pipeline with clear escalation paths and override capabilities.
Every production agent system we build includes human oversight. TalentSync AI gives recruiters structured screening summaries with one-click override agents act autonomously, every decision is reversible. WorkflowAI pauses execution at configured approval nodes and routes to designated approvers via Slack or email. PatientFlow AI requires attending physician confirmation for high-acuity bed transfers. The agent assists. The human decides. Always.
About 80% of AI products work perfectly with a single model and good prompt engineering. The 20% that need agents products where accuracy above 90% isn’t optional, where tasks span multiple systems, where mistakes carry real consequences — that’s where we operate.
Technology
Stateful multi-agent workflows with explicit state management, conditional branching, and persistent context across sessions. The production standard for multi-agent orchestration.
Agent observability, debugging, and performance monitoring. Per-agent tracing with input, output, latency, token usage, and decision rationale.
Complex reasoning agents screening scoring, impact assessment, clinical NLP, multi-step analysis.
Compliance-sensitive agents regulatory classification, FCA boundary management, structured data extraction.
Document understanding agents resume parsing, damage assessment, multimodal analysis.
Each agent gets the LLM best suited to its specific task not a single model for everything.
Agents read and write to PostgreSQL, MongoDB, and Qdrant with explicit per-agent permissions.
Google Calendar, Guidewire, TrueLayer, Epic FHIR each integration production-hardened with retry logic and dead-letter queues.
An agent that reads from the vector database cannot write to the CMS. Principle of least privilege, applied to AI.
Per-user thread isolation for conversation-oriented agents.
Persistent state for long-running workflows that span hours or days.
Real-time coordination signals between agents operating simultaneously.
Agent architecture, orchestration patterns, accuracy control, running costs the questions every founder asks before building a multi-agent system.
Not every product needs multi-agent AI. But the products that do where accuracy above 90% isn’t optional, where tasks span multiple systems, where mistakes carry real consequences that’s exactly where we operate. If you’re not sure which category your product falls into, that’s exactly the conversation we should have.