Autonomous AI Agents for Enterprise Workflows
Iedeo builds production-grade autonomous AI agents that go beyond chat — they reason, take actions across your systems, and complete real work. From sales-development bots to back-office automation, we ship agents that move from POC to production.
Why most AI agent projects fail in production
Hallucinated actions
Naïve LLM agents take wrong actions, send wrong emails, update wrong records — without strong guardrails.
Brittle tool calling
Agents that call 5+ tools chain failures rapidly. Most demos collapse on the 3rd step.
No human-in-the-loop
Production agents need clear escalation, approval gates, and audit trails — not vibes.
No retry/recovery
When a tool fails, naïve agents loop forever or give up — costing tokens and trust.
Untestable behaviour
You cannot ship what you cannot test — agent QA needs deterministic test harnesses.
Permission sprawl
Agents with admin access to every system are a security and compliance disaster.
Iedeo agent platform — built for production
We do not ship LangChain demos. Our agents are architected with explicit memory, scoped tool access, observable execution, and human gates.
Multi-step reasoning
Agents plan, decompose tasks, call tools, observe results, and re-plan — using Claude, GPT-4, or fine-tuned models.
- Tree-of-thought planning
- ReAct + reflection patterns
- Custom planner per domain
Scoped tool access
Each agent has explicit, least-privilege access to specific tools — CRM read-only, ticketing write, etc.
- Per-tool authentication
- Action audit logging
- Permission scopes per agent
Human-in-the-loop
Critical actions trigger Slack/email approval. Agents pause until human confirms.
- Approval gates configurable
- Slack/Teams integration
- Full conversation review
Full observability
Every step logged — plan, tool call, observation, output. Replay any conversation in seconds.
- Trace UI for debugging
- Cost & latency per step
- Slack alerts on anomalies
Long-term memory
Agents remember past conversations, user preferences, and learned patterns across sessions.
- Vector + relational memory
- Per-user / per-tenant
- Memory pruning & summarisation
Domain-tuned
We fine-tune planners and tool-use patterns on your domain — not generic.
- Custom system prompts
- Domain-specific eval sets
- Continuous improvement loop
How we deliver production-grade agents
Workflow audit
We map a single end-to-end workflow — sales lead enrichment, support ticket resolution, expense approval — and identify exactly where AI can act vs. where humans must.
Tool & data inventory
List every system the agent must touch. Define read vs write scopes. Set up authentication and least-privilege credentials.
Agent design
Choose architecture (single-agent vs multi-agent), planner pattern (ReAct, plan-execute, reflection), and memory model. Write the system prompt.
Eval suite first
Before any production traffic, we build 50-200 evaluation scenarios covering happy paths, edge cases, and failure modes. Agents must pass evals to ship.
Pilot & graduate
Run with human approval on every action for 2 weeks. Promote actions to auto-approved as evals stay green. Reach full autonomy by week 6-8.
Industries We Serve
Sales Development
Lead enrichment, outbound personalisation, CRM hygiene, follow-up automation.
Customer Support
L1 ticket triage, knowledge-base answer, refund/return automation, escalation.
Back-office
Invoice processing, expense approval, vendor onboarding, contract review.
HR & Recruiting
Candidate screening, interview scheduling, onboarding workflows, policy Q&A.
Data & Analytics
Natural-language reporting, anomaly investigation, dashboard generation.
E-commerce Ops
Order issue resolution, supplier coordination, inventory alerts.
Finance
Reconciliation, treasury alerts, expense policy enforcement, audit prep.
Legal & Compliance
Contract review, policy Q&A, compliance check, document retrieval.
Why enterprises hire Iedeo for agent projects
Production track record
We have shipped agents handling thousands of decisions per day in regulated and unregulated industries.
Framework-agnostic
We use LangGraph, OpenAI SDK, custom orchestration — whichever fits the problem. We are not selling you a framework.
Evals are non-negotiable
We build evaluation harnesses before agents go to prod. You can prove improvement quarter over quarter.
On-prem & VPC capable
For sensitive domains, agents run fully in your tenant with self-hosted LLMs (Llama 3, Mistral, Mixtral).
Cost-engineered
We tune prompt length, cache responses, route easy tasks to small models. Production agents cost ₹2-15 per task, not ₹500.
Real engineering, not no-code
No fragile drag-and-drop builders. Real code, real tests, real CI/CD, real rollback.
Frequently Asked Questions
Common questions about our services and technology.
What is an autonomous AI agent vs a chatbot?
A chatbot answers questions. An autonomous agent takes actions — reads your CRM, sends emails, files tickets, processes refunds — by reasoning across tools. Agents complete workflows; chatbots respond to messages.
How long does it take to ship an agent to production?
A focused single-workflow agent (e.g., support ticket triage) ships to production in 8-12 weeks. Multi-agent platforms with several workflows typically take 14-20 weeks.
How do you prevent the agent from taking wrong actions?
Three layers: (1) least-privilege tool scopes, (2) approval gates for critical actions during pilot, (3) eval suite that must stay green before promoting actions to auto-approved.
What LLMs do you use for agent projects?
GPT-4 (Turbo and 4o), Claude (Sonnet and Opus), Gemini 1.5 Pro, and open-source (Llama 3 70B, Mixtral 8x22B, fine-tuned Mistral 7B). We pick per use case based on cost, accuracy and data-residency requirements.
Can the agents run on-prem or fully in our cloud?
Yes. For regulated industries we deploy with self-hosted open-source LLMs and full air-gap if required. All orchestration runs in your VPC.
How much does an enterprise agent project cost?
Single-workflow agent: ₹10-25L. Multi-agent platform: ₹30-75L. Per-task inference cost in production: ₹2-15 depending on complexity and LLM choice. We give fixed-scope quotes after discovery.
What happens if the LLM provider goes down?
We architect agents to fail over across providers (OpenAI → Anthropic → self-hosted) with graceful degradation. Multi-LLM routing is standard in our agent platform.
Ship your first production agent in 8 weeks
Book a 30-minute call with our agent architects. We will identify one high-ROI workflow, scope the project, and share evaluations from comparable production deployments.