Agentic AI is redefining how software behaves—enabling applications to act autonomously, plan multi-step actions, and make decisions using the power of Large Language Models (LLMs). At Codehall Technologies, we specialize in building agentic systems that go beyond simple prompts. Our solutions combine LLMs, reasoning frameworks, memory, tool integrations, and orchestration layers to create intelligent, goal-driven agents for real-world use cases—from copilots and chatbots to autonomous workflows and task automation. Whether you're looking to build a customer support agent, research assistant, or autonomous decision-maker, we help you integrate LLMs safely and effectively into your product ecosystem
Read Case StudyAt the heart of every agent is an LLM (such as GPT-4, Claude, Gemini, or open-weight models like LLaMA). We architect the system around the LLM’s reasoning capabilities while controlling its interactions, planning steps, and tool usage.
We support and integrate multiple LLM providers, enabling dynamic switching between models based on cost, latency, or content type. This avoids vendor lock-in and allows for performance tuning at scale.
We implement detailed monitoring of all LLM calls—including tokens used, API latency, and error rates. Custom dashboards allow you to track usage per user, feature, or environment, helping control spend and optimize performance.
Our agents can break down goals into steps, call APIs, evaluate outcomes, and iterate independently. Using LLM reasoning, they dynamically generate plans and revise them based on feedback and memory.
Every agent interaction is wrapped with safeguards—validating inputs, controlling output actions, and allowing human-in-the-loop approvals. We define strict permission layers for real-world API interactions.
Our agents remember past conversations, tasks, and user preferences. We combine vector databases and structured memory to give LLMs richer context, allowing them to reason over time and personalize responses.
We build agents that interact with users and systems using LLM reasoning. These agents can answer questions, complete tasks, analyze data, and drive workflows—all while managing context and adapting behavior over time.
We enable agents to take action by integrating them with tools like webhooks, databases, CRMs, search engines, file systems, and internal APIs. Every tool use is gated by logic and monitored for safety.
We implement memory layers for persistent knowledge—letting agents recall user preferences, past decisions, and ongoing tasks. This enables continuity and more intelligent decision-making across sessions.
We build complex systems involving multiple agents or agents that coordinate between sub-tasks. Whether orchestrating workflows or assigning specialized subtasks, we support advanced reasoning across agent networks.
We build internal evaluator agents or external review pipelines to assess LLM outputs. This includes factual correctness, tone, relevance, and ethical alignment—allowing agents to self-correct or escalate when needed.
Our deployed agents are production-grade—containerized, version-controlled, monitored, and auditable. We monitor usage patterns, manage LLM cost overhead, and offer kill switches or manual overrides for sensitive operations.
LangChain forms the foundation of our agent workflows—supporting prompt templates, tool integration, retrieval, and chains of thought. With LangGraph, we move beyond linear sequences into dynamic, stateful graphs that enable decision-based branching, parallel execution, retries, and memory sharing between agents. This allows us to model complex agent behaviors, including planning, recursive delegation, and interaction loops.
AutoGen enables multi-agent conversations with reasoning control and custom role behaviors. CrewAI helps us organize agents into structured roles like Researchers, Planners, and Executors. These frameworks enable both collaborative and hierarchical workflows—where agents communicate, critique, and refine outputs in iterative loops. Ideal for content generation, research automation, and autonomous task planning.
We use vector databases like Pinecone and Qdrant to persist long-term memory and enable semantic search. These systems store embeddings for documents, conversations, and actions—allowing agents to retrieve relevant context efficiently. Our memory modules are optimized for fast recall, chunk relevance, and cross-agent memory sharing to improve contextual grounding and reduce repetition.
LangSmith provides deep observability into agent execution: tracking prompt flows, input/output history, intermediate steps, and errors. PromptLayer augments this with version control for prompts, prompt metrics, and token-level cost tracking. Together, they enable fine-grained debugging, A/B testing of prompt variants, and ongoing quality tuning of agent behavior.
To ensure safety, structure, and policy compliance, we integrate Guardrails AI for output validation and format enforcement. Rebuff enables runtime protection against prompt injection and unsafe tool usage. We also build custom guardrails based on JSON schemas, regex filters, and domain-specific rules to enforce boundaries for sensitive applications like healthcare, finance, and education.
We support pluggable integration with multiple LLM providers including OpenAI (GPT-4, GPT-4o), Anthropic Claude, and Google Gemini. Depending on your use case, we help you optimize for reasoning performance, latency, cost, or data privacy. Our routing layer enables fallback models, auto-selection by capability, and hybrid responses across models—all while tracking model usage, quality, and spend per agent or task.
We identify the right opportunities for agent-driven automation, define the agent's objectives, and design its interaction scope, memory needs, and reasoning complexity.
Based on performance, compliance, and budget goals, we select one or more LLMs and design a modular agent architecture—planning flows, memory, evaluation, and action interfaces.
We build and test prototypes in sandboxed environments, evaluating how the agent reasons, makes decisions, handles edge cases, and interacts with tools.
We securely integrate agents with internal or external systems. This includes APIs, search, files, databases, and CRMs—with clearly scoped permissions and fallback handling.
We test agent behavior under various prompts and scenarios. We set up usage monitoring, cost alerts, logging, and feedback pipelines to ensure accountability and alignment.
Agents are launched into production or pilot environments with live user feedback capture. Human reviewers or evaluators can guide the agent's evolution post-launch.
We continuously tune prompts, memory strategies, cost-performance trade-offs, and add new capabilities—keeping your agents relevant, efficient, and aligned with your business goals.