Agents vs. Chatbots: The Difference Matters
A chatbot answers questions. An agent takes action.
Most "AI agents" are just ChatGPT wrappers with a fancy UI. Real agents can:
- Use tools and APIs
- Make decisions autonomously
- Handle multi-step workflows
- Integrate with your existing systems
- Recover from errors gracefully
- Work alongside humans effectively
That's what we build.
When You Need an Agent
✅ Build an Agent When:
- Repetitive workflows — Tasks humans do manually that follow patterns
- System integration — Need to coordinate across multiple tools/APIs
- Decision-making — Process that requires judgment based on data
- 24/7 availability — Work that needs to happen around the clock
- Volume scaling — More work than your team can handle
❌ Don't Build an Agent When:
- A simple automation/script would work (don't over-engineer)
- The task requires true creativity (AI is pattern-matching, not creative genius)
- Failures would be catastrophic (start with human-in-loop)
- You can't define success criteria (agents need clear goals)
Agent Architectures We Build
ReAct (Reasoning + Acting)
Agent reasons about what to do, then takes action. Best for:
- Multi-step problem solving
- Research and data gathering
- Complex decision-making workflows
Tool-Using Agents
Agent decides which tools to use and how. Tools can be:
- APIs (REST, GraphQL)
- Databases (SQL, NoSQL)
- Search engines
- Internal systems (CRM, ERP, etc.)
- Code execution environments
Multi-Agent Systems
Multiple specialized agents working together:
- Horizontal coordination — Agents collaborate as peers
- Hierarchical delegation — Manager agent delegates to worker agents
- Pipeline/workflow — Agent A's output feeds Agent B
Human-in-the-Loop Agents
Agent proposes actions, human approves. Critical for:
- High-stakes decisions (financial, legal, medical)
- Learning phase (agent improves from human corrections)
- Auditability requirements
- Gradual trust-building
Real-World Agent Applications
Customer Support Triage Agent
What it does:
- Reads incoming support tickets
- Classifies urgency and category
- Searches knowledge base for solutions
- Drafts responses for human review
- Escalates complex issues to humans
- Updates CRM with ticket status
Result: 70% of tickets handled without human intervention, 50% faster response times.
Sales Research Agent
What it does:
- Given a company name, researches key information
- Finds decision-makers on LinkedIn
- Analyzes company financials and news
- Identifies pain points and opportunities
- Generates personalized outreach draft
- Populates CRM with enriched data
Result: 10x faster account research, SDRs focus on conversations not data entry.
DevOps Incident Response Agent
What it does:
- Monitors alerts from observability platforms
- Triages incidents by severity
- Gathers diagnostic info (logs, metrics, traces)
- Searches past incidents for similar patterns
- Suggests remediation steps
- Executes safe recovery actions (restart services, scale resources)
- Notifies on-call engineer with context
Result: Mean time to resolution reduced by 40%, fewer 3am wake-ups.
Content Moderation Agent
What it does:
- Reviews user-generated content for policy violations
- Flags potential issues (spam, abuse, illegal content)
- Routes edge cases to human moderators
- Learns from human decisions to improve accuracy
- Generates reports on moderation trends
Result: 95% accuracy, human moderators only review 5% of content.
Financial Report Generation Agent
What it does:
- Pulls data from multiple financial systems
- Runs calculations and variance analysis
- Identifies anomalies and trends
- Drafts narrative explanations
- Generates charts and visualizations
- Formats into board-ready presentation
Result: Report generation time from 3 days to 3 hours.
Our Agent Development Process
Phase 1: Discovery & Design (Weeks 1-2)
- Map current workflow and pain points
- Define agent's scope and responsibilities
- Identify integration points (systems, APIs, databases)
- Design agent architecture (ReAct, tool-use, multi-agent)
- Define success metrics and failure modes
- Determine autonomy level (fully autonomous vs human-in-loop)
Phase 2: Tool & Integration Development (Weeks 2-3)
- Build connectors for external systems
- Create tool wrappers with proper error handling
- Implement authentication and authorization
- Set up observability and logging
Phase 3: Agent Development (Weeks 3-5)
- Implement core agent logic (planning, reasoning, action)
- Build decision-making workflows
- Create system prompts and few-shot examples
- Implement error recovery and fallback strategies
- Add human-in-loop controls where needed
Phase 4: Testing & Refinement (Week 5-6)
- Unit tests for tools and components
- End-to-end workflow testing
- Red team testing (adversarial inputs)
- Performance and latency optimization
- Prompt engineering and tuning
Phase 5: Deployment & Monitoring (Week 6+)
- Gradual rollout (shadow mode → limited pilot → full production)
- Real-time monitoring dashboards
- Alert setup for failures and anomalies
- Feedback collection from users
- Iterative improvement based on real-world performance
Technology Stack
Agent Frameworks
- LangChain / LangGraph — Most mature agent framework
- AutoGPT / BabyAGI — Autonomous agent patterns
- Semantic Kernel — Microsoft's agent framework
- Custom implementations — When off-the-shelf doesn't fit
LLM Providers
- OpenAI (GPT-4, GPT-3.5) — Powerful, reliable
- Anthropic Claude — Long context, strong reasoning
- Open-source (Llama, Mistral) — For self-hosted, cost-sensitive use cases
- Hybrid — Different models for different tasks
Integration & Orchestration
- Workflow engines: Temporal, Airflow, Prefect
- Message queues: RabbitMQ, Kafka, SQS
- Databases: PostgreSQL, MongoDB, Redis
- API management: Kong, Apigee
Observability
- LLM observability: LangSmith, Helicone, Weights & Biases
- Application monitoring: Datadog, New Relic, Prometheus
- Logging: ELK Stack, CloudWatch
- Tracing: Jaeger, OpenTelemetry
Autonomous vs. Human-in-the-Loop
Start with Human-in-the-Loop
We recommend beginning with agents that propose actions for human approval:
- Build trust gradually
- Agent learns from corrections
- Safer for high-stakes operations
- Clear audit trail
Move to Autonomy When:
- Agent accuracy consistently >95%
- Failure impact is manageable
- Rollback/undo mechanisms exist
- Monitoring can catch issues quickly
Multi-Agent Patterns
Specialist Agents with Manager
One manager agent delegates tasks to specialist agents:
- Customer support: triage agent → technical agent, billing agent, or general agent
- Research: manager → web search agent, document analysis agent, synthesis agent
Sequential Pipeline
Agents in a production line, each doing one step:
- Content creation: research agent → writing agent → editing agent → SEO agent
- Data processing: extraction agent → validation agent → enrichment agent → storage agent
Collaborative Peer Agents
Multiple agents work together as equals:
- Code review: multiple agents review different aspects (security, performance, style)
- Strategic planning: agents debate different approaches, consensus emerges
What We Deliver
- Production-ready agent — Deployed and integrated with your systems
- Tool library — Reusable connectors for future agents
- Admin dashboard — Monitor agent activity, override decisions
- Documentation — How the agent works, how to maintain it
- Training materials — For your team to use and extend the agent
- Monitoring & alerts — Know immediately if something goes wrong
Pricing
- Fixed-price agent development — Clearly scoped agent with defined capabilities
- Multi-agent system — More complex, priced per-agent with integration overhead
- Ongoing maintenance retainer — Support, improvements, new tools
- Consulting & feasibility — Should you build an agent? We'll give honest advice.
Ready to Build Your Agent?
Book a discovery call. We'll map your workflow, identify automation opportunities, and design an agent that actually delivers value.
We don't build agents for the sake of AI hype. We build agents that save time, reduce costs, and let humans focus on higher-value work.