AI Agents
Autonomous systems that plan, reason, and execute multi-step workflows - with tool use, memory, and human oversight where it matters.
AI agents go beyond single-prompt chatbots. They break complex tasks into steps, call external tools (APIs, databases, email, CRMs), maintain context across interactions, and know when to escalate to a human.
Who It's For
Is This the Right Fit?
Teams with multi-step workflows that need autonomous execution, tool integrations, and human oversight at critical checkpoints.
Deliverables
What You Get
- Agent architecture design (single vs. multi-agent)
- Tool integration with existing systems
- Memory and state management
- Human-in-the-loop checkpoints
- Monitoring, logging, and cost controls
- Production deployment with documentation
Typical Engagement
$30,000 – $75,000
8–12 weeks
Use Cases
Common Applications
- Lead qualification and routing agents that research prospects and update your CRM
- Document processing agents that extract, classify, and route information
- Internal ops agents for scheduling, reporting, and cross-platform data sync
- Customer support agents that resolve tier-1 issues with full context
- Research agents that synthesize information into actionable briefs
FAQ
Frequently Asked Questions
How do you decide between a single agent and multi-agent architecture?
We map your workflow complexity, integration surface, and failure modes in discovery. Simple sequential tasks often need one agent; parallel workstreams or specialized roles benefit from multi-agent orchestration.
What human oversight is built in?
Every agent includes configurable checkpoints - approval gates for high-risk actions, escalation paths, and audit logs. You control where humans stay in the loop.
How do you control LLM costs at scale?
We implement caching, model routing (smaller models for simple steps), token budgets, and monitoring dashboards so costs stay predictable as usage grows.
Ready to scope ai agents?
Book a discovery call or send a message with your use case.