Agentic LLMs
All words and no actions turn LLMs into Large Liability Models. Thus Enter the Agentic LLMs.
Large Language Models are powerful at understanding and generating language. However, language alone is not enough to solve real-world problems.
Agentic LLMs extend traditional LLMs by giving them the ability to decide, act, observe outcomes, and adapt — much like a human operator executing tasks step by step.
Instead of responding once and stopping, an agent reasons over multiple steps, uses tools, and works toward a goal.
Why Agentic LLMs Exist
A normal LLM answers questions.
An agentic LLM answers questions and then does something about it.
Examples:
Reading a document → extracting facts → validating them → storing results
Understanding a user request → choosing tools → calling APIs → verifying outputs
Planning a multi-step task → executing → correcting mistakes → finishing the goal
Agentic behavior is essential when:
The task cannot be completed in one response
The system must interact with external systems
The model must self-correct or re-plan
Core Agent Loop
At the heart of an Agentic LLM is a simple but powerful loop:
Observe Receive user input, system state, or tool outputs.
Reason Decide what to do next based on goals and context.
Act Call a tool, query a database, read a document, or ask a follow-up question.
Evaluate Check whether the action helped achieve the goal.
Repeat or Stop Continue until the objective is satisfied.
This loop turns a passive model into an active problem solver.
Tools as Extensions of Cognition
In agentic systems, tools are not add-ons — they are extensions of the model’s capabilities.
Common tool types:
Search engines
Databases
Code execution
APIs
Memory stores
Document readers
The LLM does not know everything. Instead, it knows how to find, verify, and combine information.
Planning vs Execution
Agentic LLMs often separate thinking into two layers:
Planner
Breaks a goal into sub-tasks
Chooses execution order
Executor
Performs each step
Reports results back to the planner
This separation improves:
Reliability
Debuggability
Control over long-running tasks
Memory in Agentic Systems
Unlike single-turn chatbots, agents require memory.
Types of memory:
Short-term: current task context
Working memory: intermediate results
Long-term: user preferences, learned facts, prior executions
Memory allows agents to:
Avoid repeating mistakes
Maintain continuity
Learn from previous runs
Failure Is a Graceful
Agentic systems are designed to fail safely.
Instead of collapsing on errors, they:
Detect failures
Re-evaluate assumptions
Retry with alternative strategies
This is critical for:
Automation
Enterprise workflows
Mission-critical systems
Agentic LLMs and Indian Languages
Most large language models are optimized primarily for English-first interaction. However, real-world conversational systems — especially in India — require native competence across multiple Indian languages, dialects, and code-mixed usage.
At Dhee, we work with Conversational Agentic LLMs trained and adapted for Indian languages, focusing on:
Natural, spoken-style conversations
Language-specific grammar and morphology
Cultural context and usage patterns
Multi-turn dialogue and Task following robustness
These models are designed not just to translate, but to converse natively.
You can explore our open model collection here: 👉 https://huggingface.co/collections/dheeyantra/
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