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#AGENTSAI▲ FLAGSHIP

AI Agent Development

Autonomous agents that plan, use tools, and get real work done — not chatbots.

model: custom scope & deployment · contact for quote

~/telemetry/spec
RUNNING
agent_typeautonomous_orchestratororchestration_engineLangGraphinference_gatecost_capped_routerruntimepython_3.12 · node_20
NODE HEALTH0%
○ waitingmeasuring...

~/scope

6 capability modules
01#AUTONOMY

Task planning & tool use

Agents built for action — they plan multi-step tasks, call your tools, and drive them to completion without a human in the loop.

  • Task decomposition
  • Tool calling
  • ReAct loops
  • Reflection
02#MESH

Multi-agent systems

Multiple specialized agents that collaborate on complex workflows — planner, executor, reviewer — with hand-offs that don't lose context.

  • LangGraph
  • Swarm patterns
  • Hand-off protocols
  • Supervisor agents
03#MEMORY

Memory & context

Working, long-term, and semantic memory so the agent learns from every run instead of starting cold each time.

  • Vector memory
  • Summaries
  • Personalization
  • Recency
04#DEFENSE

Safety & guardrails

Cost limits, human-in-the-loop checkpoints, input/output validation, and failure handling so autonomy never means runaway spend.

  • Cost caps
  • HITL checkpoints
  • Validation
  • Fallbacks
05#TOOLS

Tool integrations

Everything your business already uses becomes an agent "tool" — CRMs, databases, internal APIs, email, and file systems.

  • Custom tools
  • OpenAPI specs
  • Auth scoping
  • Webhooks
06#TRACE

Evaluation & monitoring

Trace every step, score outcomes, and catch behavioral drift before it silently degrades in production.

  • Tracing
  • Evals
  • Alerts
  • Cost telemetry

~/stack

all runtimes production-validated

FOUNDATION

PythonNode.jsTypeScript

REASONING

OpenAIClaudeGPT-4oo1

FRAMEWORK

LangChainLangGraphCrewAIOpenAI Agents

STATE CONTROL

Mem0pgvectorRedisSemantic memory

EVENT RUNTIME

BullMQWebSocketstRPC

~/process

4-stage execution pipeline
  1. 01SCOPE

    Define exactly what the agent should (and shouldn't) do autonomously.

    output:Autonomy contract
  2. 02ARCHITECT

    Design the loop, tool access, memory layer, and stop conditions.

    output:Agent architecture diagram
  3. 03EXECUTE

    Build with real tool calls — not mocked ones — and test edge cases.

    output:Deployable agent build
  4. 04HARDEN

    Guardrails, cost limits, logging, and fallback behavior before production.

    output:Production runbook

~/work-case-study

linked to live /work routes
~/work/gpt-guiderAI learning companion

GPT Guider

A guided AI tutor that plans lessons, adapts to your pace, and routes every response through a cost-aware inference gate.

99.4%Task accuracy
<450msResponse latency
0Uncaught loops
OpenAILangChainNext.jsStreaming
~/action
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Users reached
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Uptime mindset