AI Agent Development
AI agents that do the work — not just answer.
Autonomous agents that reason over a goal, call your tools and APIs, query your data, and complete multi-step work — with humans in the loop where it matters and a full audit trail of every action.
Tool-use
Agents that take real actions
HITL
Human approvals built in
24/7
Autonomous workflows
70%
of knowledge work is repeatable, multi-step tasks
40%
of a support team’s time spent on tier-1 triage
5+
tools a single workflow often spans
< 2s
per agent step we engineer for
Why Agents
A chatbot answers. An agent acts.
Most “AI” shipped today is a chat box — it talks, but it can’t do anything. An agent is different: give it a goal and it plans the steps, calls the right tools and APIs, reads and writes to your systems, and keeps going until the job is done. That is the difference between a smarter FAQ and software that actually takes work off your team.
We build agents that are observable and controllable, not black boxes. Every action is logged, sensitive steps pause for human approval, and tool permissions are scoped tightly. You get autonomy where it’s safe and a human gate where it counts — so you can trust an agent with real work without handing over the keys.
Capabilities
Everything an agent needs to act
Goal-driven planning
Agents decompose a goal into steps, choose tools, and adapt when a step fails — not a fixed script.
Tool & API use
Connect your internal services and third-party APIs so the agent can actually take actions, not just talk.
Retrieval (RAG)
Ground decisions in your knowledge base, docs, and live data so answers and actions are accurate.
Guardrails & HITL
Scoped permissions and human-in-the-loop approvals on sensitive steps keep the agent safe.
Observability & evals
Full traces, logs, and automated evaluations so you can see and trust every decision.
Multi-agent orchestration
Specialized agents that hand off to each other for complex, multi-stage workflows.
Agent vs Chatbot
Why an agent beats a plain chatbot
A chatbot is a conversation. An agent is a worker. The gap shows up the moment real work needs doing.
| Criterion | AI Agent (Ethersofts) | Plain Chatbot |
|---|---|---|
| Takes real actions | Calls tools & APIs | Text replies only |
| Multi-step tasks | Plans & executes | One turn at a time |
| Grounded in your data | RAG + live systems | Generic / static |
| Safety on sensitive steps | Human-in-the-loop | None |
| Auditability | Full action traces | Chat log only |
How It WorksA clear path from
A clear path from
start to ship
Disciplined and transparent — weekly visibility, no black boxes, and a working result you can measure.
01
Scope
Map the task, the tools it needs, and exactly where a human must approve before action.
02
Build
Agent loop, tool integrations, memory, retrieval, and guardrails — wired to your systems.
03
Evaluate
Test against real cases with automated evals; tune reliability, cost, and latency.
04
Deploy
Ship with tracing, fallbacks, approval queues, and monitoring from day one.
Industries
Built for your sector
Customer Support
Triage, draft, and resolve tier-1 tickets end to end, escalating the rest with context.
TriageResolutionEscalation
Finance & Ops
Reconcile data across systems, flag anomalies, and prepare reports without manual shuffling.
ReconciliationReportingAnomalies
Sales & Growth
Research accounts, enrich CRM records, and draft personalized outreach at scale.
ResearchEnrichmentOutreach
Engineering
Code-aware agents that triage issues, draft fixes, and run checks inside your pipeline.
Issue triagePR draftsCI checks
Back Office
Automate data entry, document processing, and cross-tool workflows with approvals.
Data entryDocsWorkflows
Healthcare Ops
Prior-auth, scheduling, and records workflows with strict guardrails and audit logs.
Prior-authSchedulingCompliance
Example Outcome
From queue to cleared in minutes
Challenge
A B2B SaaS support team was drowning in tier-1 tickets — slow first responses, inconsistent answers, and engineers pulled in for routine lookups.
What We Built
A support agent with RAG over their docs and history, tool-use into their helpdesk and billing system, and human approval on refunds and account changes — fully traced.
Results
68%
tier-1 tickets auto-resolved
4×
faster first response
100%
actions logged & auditable
“It’s not a chatbot — it actually closes tickets. The approval gates meant we could trust it with refunds from week one, and every action is traceable.”
OS
Support Operations Lead
B2B SaaS · 40-person team
AI Agent DevelopmentPut an agent
Put an agent
to work
Autonomous agents that reason over a goal, call your tools and APIs, query your data, and complete multi-step work — with humans in the loop where it matters and a full audit trail of every action. Tell us your use case — we reply within 24 hours with a real assessment.
Free consultation24-hour responseNDA on request
FAQ
AI Agent Development questions
If yours is not here, reach out — you get a real answer from an engineer within 24 hours, not a sales pitch.
A chatbot replies with text. An agent plans, calls tools and APIs, and completes actions — booking, updating records, running workflows — with guardrails and human approvals where needed.
Yes, when built right. We scope permissions tightly, add human-in-the-loop approvals for sensitive steps, and log every action for full auditability. You decide where autonomy ends and a human gate begins.
LangGraph and LangChain for orchestration, the OpenAI and Anthropic tool-use APIs, and open-source models where cost or privacy calls for it — chosen for reliability, not hype.
Yes. We integrate via your APIs and third-party connectors — helpdesks, CRMs, databases, internal services — so the agent acts inside the systems you already run.
Scoped tool permissions, validation on every step, approval gates, automated evals, and full tracing. If something looks wrong, it stops and escalates to a human.
A focused, production agent is typically 4–8 weeks depending on the number of tools and approval flows. We start with one high-value workflow and expand.
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