AI Agent Development Services We Offer in Tel Aviv
Tel Aviv AI-agent work splits between cybersecurity-vendor internal copilots and customer-facing assistants (Check Point's Infinity AI Copilot, Wiz Code, CyberArk's Conjur and Privilege Cloud agents, SentinelOne's Purple AI, Cybereason's MalOp investigation agents — where the agent is operating inside the security product itself and the security-of-the-agent question is non-trivial), fintech and consumer-finance agents for Pagaya, Lemonade, Payoneer, eToro, Riskified, Melio, Tipalti, and Rapyd under Bank of Israel and Israel Securities Authority oversight, healthtech agents for the Sheba ARC innovation programme, K Health, Healthy.io, and the Clalit-Maccabi-Meuhedet-Leumit health-fund integrations, and SaaS scaleup workflow agents for the broader Israeli software ecosystem (monday.com, Wix, Fiverr, Lightricks, Riskified). We build with the right framework per problem: LangChain and LlamaIndex for retrieval-heavy patterns where the ecosystem maturity wins, Microsoft AutoGen for multi-agent orchestration where the structured-conversation pattern fits, OpenAI Assistants and Anthropic computer-use for the closed-frontier-model path where the product team accepts the vendor dependency, and AI21 Labs Jamba (built in Tel Aviv) for clients who want an Israeli-developed foundation model. Every agent ships with a documented prompt-injection threat model, a tool-execution-sandbox security review, and a Privacy Protection Authority-aligned data-flow diagram.
Our AI Agent Development Development Process
Discovery, design, build, and deployment run on Israeli IST hours with structured handoffs into our New York EST and Chandigarh IST hubs so Tel Aviv product, security, and compliance leads at Check Point, Wiz, CyberArk, SentinelOne, Cybereason, Pagaya, Lemonade, Payoneer, eToro, or Riskified get synchronous standups on the Israeli Sunday-to-Thursday working week. Discovery opens with an adversarial threat-modelling session covering the OWASP Top 10 for Large Language Model Applications (LLM01 Prompt Injection through LLM10 Model Theft), the Microsoft AI Red Team threat taxonomy, the NIST AI Risk Management Framework (AI RMF 1.0) Govern-Map-Measure-Manage functions, and the MITRE ATLAS adversarial-machine-learning tactics framework. Privacy Protection Law Amendment 13 review classifies every data type that flows through the agent's memory, retrieval index, tool-execution sandbox, and conversation log. Bank of Israel or Israel Securities Authority sector overlays apply where in scope. Build sprints run two weeks against a documented evaluation harness (Promptfoo, OpenAI Evals, Anthropic's evaluation framework, or DeepEval) that gates production deployment on a regression-test pass rather than a vibe check. Every agent ships with a structured logging trail to a SIEM (Splunk, Coralogix, Sumo Logic, or the Wazuh open-source path where the client risk posture allows it) so the security team can run the same adversarial replay testing post-launch that the build team ran pre-launch.
Process Discovery
1-2 WeeksWe sit with the people doing the work in {city} and record the real process — including the exceptions they handle by instinct, which are exactly what kill naive automations.
Tool Surface Design
1-2 WeeksEvery system the agent touches gets a typed, permission-scoped tool with its own rate limit and rollback path. The agent gets a narrow set of verbs, never raw admin access.
Build & Evaluate
3-6 WeeksThe agent is built alongside its evaluation suite from day one, using real tasks from your business with verified outcomes. Every change is scored before it ships.
Shadow Mode
2-3 WeeksThe agent runs against live traffic but commits nothing. We compare its proposed actions to what your team actually did and tune until agreement is high enough to trust.
Staged Autonomy & Run
OngoingAutonomy is released by risk band — reversible actions first, irreversible ones keeping a permanent human gate. Then we monitor completion rate, escalations, latency and spend.
Technologies We Use for AI Agent Development
Our default Tel Aviv agent stack runs LangChain or LlamaIndex for the retrieval-augmented-generation orchestration layer where the ecosystem maturity and Python or JavaScript SDK tooling win, Microsoft AutoGen or LangGraph for multi-agent state-machine patterns where the conversation flow benefits from explicit graph modelling, and direct OpenAI Assistants, Anthropic Claude, or AI21 Jamba (the Tel Aviv-built foundation model) API calls where the abstraction overhead is not justified. Vector stores default to Pinecone, Weaviate (the Dutch open-source vector DB with strong Israeli adoption), Qdrant, or Chroma with Israeli or EU regional deployment. Embedding models default to OpenAI text-embedding-3, Cohere embed v3, or Nomic embed where on-premises is required. Tool-execution sandboxes run in AWS Lambda inside il-central-1 (Tel Aviv), Cloud Run in me-west1 (Tel Aviv), or Azure Container Apps in Israel Central, with the agent's tool-execution scope bound by IAM principal-of-least-privilege and the network egress restricted to documented allowlists. Observability runs through Langfuse, LangSmith, or the open-source Phoenix stack where the client risk posture requires self-hosted. Adversarial testing runs through Promptfoo, Garak, and PyRIT (Microsoft AI Red Team's Python Risk Identification Toolkit) on every release before the agent reaches a customer-facing surface.
Other Services We Offer in Tel Aviv
Looking for a different service? Explore our full range of technology solutions available in Tel Aviv.
Explore Our AI Agent Development Specializations
Dive deeper into our specialized ai agent development offerings.
AI Agent Development in Other Cities
We deliver ai agent development solutions across 45 cities in 24 countries. Find a location near you.
Latest Work
Drag to explore or use arrow keys