Deep Agents handle complex workflows end-to-end—planning, executing, checking, and improving.
They’re designed for multi-step work with
tools, memory, and built-in verification.
- Research Agent
- Auditor Agent
- Synthesis Agent
- Fact-check Agent
- Citation Agent
Why Deep Agents
From Assistant to Analyst
Deep Agents don’t just respond—they research, cross-check, and produce structured outputs.
Better than “just a better model”
You don’t need to wait for the perfect model. Deep Agents help standard LLMs solve harder problems using scaffolding, tools, and checks.
Cost per outcome improves
They may use more compute per task, but reduce human review loops and rework—so total cost per outcome drops.
Modular and easier to debug
Work is split into specialist sub-agents, so you can improve one part (e.g., citations or formatting) without rewriting the whole system.
How we build your first AI co-worker?
Planning loops, not one-shot prompts
Deep Agents break a goal into steps, execute them, check results, and refine. This enables non-linear work that mirrors how teams operate.
Tool orchestration and a workspace
Deep Agents use your tools and systems safely—databases, CRMs, ticketing, internal docs—and maintain a workspace (files, notes, intermediate outputs) while working.
Verification built into the workflow
Deep Agents include checks like fact validation, source grounding, policy rules, and QA scorecards so results are reliable before they reach a human.
Verification built into the workflow
Deep Agents include checks like fact validation, source grounding, policy rules, and QA scorecards so results are reliable before they reach a human.
Where Deep Agents fit best
YAY!
Research + reporting with citations, summaries, and structured outputs
Cross-system workflows (docs + CRM + tickets + dashboards)
Compliance-heavy processes needing traceability and approvals
High-volume operations where consistency matters across teams
NAY!
Simple FAQ chatbots
Low-stakes, one-shot replies
Tasks without clear success criteria
High-risk compliance work
Deep Agents run on Safe AgentOps so
higher autonomy stays controlled.
SAFE AgentOps
Underlying layer
Legal Assist: Pilot Agent
Avesta Labs partnered with India Avenues in Australia to create their first AI-pilot to analyse assets under management with guardrails and business context.
A schematic view of a typical Deep Agent
Start small, scale with confidence.
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