AutoGen developers build conversation-driven multi-agent systems where agents exchange messages, select who speaks next, and frequently write and execute code inside sandboxes. Companies hire them because code execution demands genuine container isolation, group chats need termination conditions to stop running, and the asynchronous rewrite of the framework changed the programming model substantially.
An agent that writes and runs code is the most useful and most dangerous pattern in this field. Done properly it replaces hours of analysis work. Done casually it executes arbitrary instructions on your infrastructure. TechEsperto Solutions provides developers who build the second case out of existence.
Agents converse rather than following a defined pipeline, which means someone must decide who speaks next and when the conversation ends. And agents commonly generate and execute code, which turns a prompt injection into a remote execution risk unless isolation is properly configured. Add a major asynchronous rewrite that invalidated a great deal of published guidance, and the result is a framework with excellent capability and a wide gap between working and safe. TechEsperto Solutions supplies developers who close that gap deliberately.
Our engagements concentrate on group chat systems, sandboxed code execution, migration onto the current runtime, and hardening of existing implementations that work but are not safe to expose. Analysis agents that write code are the most common request, particularly from teams whose data questions arrive faster than their analysts can answer them. Where the output feeds forecasting, this is often combined with our predictive analytics work.
Multi-agent conversations where turn allocation follows a defined strategy, message caps are enforced, and termination is detected reliably. Built so a transcript can be read and understood afterwards.
Agents that write, run, and iterate on code against your data inside isolated containers. Suited to analysis questions where the approach cannot be specified in advance.
Inherited implementations rebuilt against the asynchronous architecture, with behaviour compared against the original throughout. Sequenced so the existing version keeps working until the replacement is proven.
Conversations that spawn sub-conversations for bounded subtasks, with defined interfaces between them. Keeps individual chats short, which improves both cost and comprehensibility.
Agents running across processes or machines where workloads justify it, with message transport, failure handling, and observability configured for that topology rather than assumed.
Adding isolation, termination conditions, cost ceilings, logging, and measured output quality to something already working. Frequently the highest-value engagement available on this framework.
Assessment for this work tests security thinking as much as framework familiarity. A developer who has run code-executing agents only on a local machine has not confronted the decisions that matter. Our screening covers isolation configuration, speaker selection trade-offs, termination reliability, and asynchronous debugging. Because execution environments are infrastructure work, projects are frequently staffed alongside our DevOps services team.
Round robin is predictable and sometimes wasteful. Model-selected allocation adapts and costs an extra call per turn. Custom functions give full control at the price of maintenance. We choose against measured behaviour rather than by preference.
Multiple stopping conditions layered together: explicit completion signals, maximum turns, token budgets, and timeout. Relying on a single condition is how conversations run far longer than anyone intended.
Purpose-built execution images with pinned packages, no credentials, and no host mounts. Rebuilt through your normal pipeline so what agents run inside is a reviewed artefact rather than an ad hoc environment.
Event subscriptions, message correlation, backpressure, and error propagation across concurrent agents. Getting this right is what makes a distributed deployment debuggable rather than merely functional.
Deciding which actions execute unattended and which pause for confirmation, placed before irreversible operations. Reviewed explicitly before launch rather than inherited from development settings.
What an agent remembers between conversations, where it is stored, and how long it lives. Unbounded retention degrades relevance and inflates every subsequent prompt, so policy is a design decision.
Most engagements start with an audit, because existing systems on this framework commonly have an isolation problem worth knowing about immediately. Options then include group chat builds, sandbox implementation as standalone work, migration onto the current runtime, an embedded engineer, and a hardening retainer. Larger programmes are staffed through our dedicated development team model. Transparent pricing, an executed NDA, and full intellectual property transfer apply throughout.
We review your implementation and report on execution isolation, termination reliability, cost exposure, logging gaps, and version risk. Delivered as a written document you can act on with your own team.
A multi-agent conversation delivered properly: defined speaker selection, layered termination conditions, cost ceilings, structured logging, and measured output quality against a reference set.
Standalone work building the isolated environment: container image, egress allowlist, resource limits, timeout enforcement, output constraints, and execution audit logging. Often the fastest risk reduction available.
Rebuilding earlier-generation implementations against the asynchronous architecture with behaviour verification throughout. Includes documented version pinning so the next upgrade is planned rather than forced.
One specialist inside your sprints for continuous development. Your team absorbs isolation and termination practice through daily contact, which builds capability that stays afterwards.
Covering execution image maintenance, dependency updates, version migrations, cost review, and incident response. Sized to actual need rather than bundled into a build contract.
This is the part of an AutoGen engagement we refuse to compromise on. Generated code runs in a container built for that purpose, never on the host and never in a general-purpose environment with credentials available. Network egress is restricted to an allowlist. Filesystem access is scoped and output size is capped. Every execution carries a timeout and resource ceiling, and every statement is logged. None of this is difficult, and its absence is the single most common finding in the audits we perform. Clients working with TechEsperto Solutions get this configured before anything reaches a shared environment.
A dedicated execution container with no host mounts, no cloud credentials, and no access to internal services beyond what the task requires. If an agent can reach your database directly, the design is wrong.
A fixed package set built through your pipeline, so agents cannot install arbitrary libraries at runtime. This also makes analysis reproducible, which matters as much for correctness as for security.
Outbound access denied by default and opened only to specific endpoints the work requires. Prevents both data exfiltration and agents fetching code from wherever a model suggested.
Read access limited to supplied data, write access limited to a working directory, and caps on output size. Stops a generated loop filling a volume or returning a payload that breaks downstream handling.
CPU, memory, and wall clock limits on every run. An agent writing an accidental infinite loop should produce an error within seconds rather than consuming a node for an hour.
Complete record of what was run, by which agent, against what data, with what result. Required for investigation and usually required for compliance review as well.
The pattern earns its complexity where the method cannot be specified in advance. If you know exactly which transformation to apply, write the function. If the question is exploratory and the approach depends on what the data reveals, an agent that writes, runs, inspects, and revises code is genuinely faster than a queue of analyst requests. Our developers have built these for reporting, pipeline repair, engineering computation, financial scenarios, code migration, and optimisation problems. Results frequently feed into our dashboard development work so findings reach the people who need them.
Questions arriving faster than analysts can answer them, where each requires a different approach. Agents produce the analysis with the code attached, so a human can verify the method rather than trusting a number.
Diagnosing malformed inputs, writing corrective transformations, and validating results. Messy real-world data is exactly the case where iterating on code beats specifying it upfront.
Parameter sweeps, unit conversion, tolerance checking, and result comparison against expected ranges. Reproducibility matters heavily, which is why pinned execution environments are essential here.
Sensitivity analysis, scenario generation, and reconciliation checks. Every output must be traceable to the code that produced it, since no financial figure should rest on an unexplained model response.
Producing test coverage for untested modules and translating between languages or framework versions. The agent can run what it writes, which raises the standard considerably over generation alone.
Scheduling, routing, and allocation problems where formulation is iterative. Agents can try several approaches and compare results faster than a person adjusting a model by hand.
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Conversational architectures can consume a surprising amount on a single stuck exchange, and these limits make that impossible rather than unlikely.
The entry point is a free audit. Give us read access under NDA and we return a written assessment covering execution isolation, termination reliability, cost exposure, logging, and version risk, with findings ranked by severity against effort. Sandbox hardening is available at a fixed price where scope is clear, and it is usually the first thing we recommend. Your engineers interview the matched developers, onboarding completes inside a week, and retainers stay optional.
A few days of examination followed by a written report. Where the finding is that generated code runs with more access than it should, you will hear it plainly and can act with your own team.
Issues ranked by severity, specific remediations named, and a staged upgrade route from your current version. Detailed enough for your developers to implement independently.
Container image, egress policy, resource limits, timeout enforcement, and audit logging delivered against defined acceptance criteria at a fixed price. Budget certainty on work that genuinely should not wait.
Profiles arrive with relevant conversational agent and code execution experience. Your team assesses them directly, declines at no cost, and matching continues until the fit is right.
Repository access, container registry, provider credentials, sample data, and sprint planning handled immediately so meaningful work lands inside the first fortnight.
Support starts light and expands only when usage justifies it. Execution image maintenance and version migration are the components clients take up first, since both recur predictably.
Our work and story have been picked up by news outlets and databases worldwide.
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Audits are free. Sandbox hardening is quoted at a fixed price once scope is defined, group chat builds are priced against acceptance criteria covering output quality and cost per run, and embedded engineers are quoted monthly. Model consumption and container hosting are billed to your own accounts with nothing added by us.
Sandbox hardening on an existing system typically takes two to three weeks. A group chat build with code execution reaches production in four to seven weeks including evaluation setup. Migrations onto the current runtime run four to eight weeks depending on how much of the original design survives.
Group chat builds usually run with one senior engineer plus infrastructure support for the execution environment. Migrations are commonly a single-person exercise, since coherence matters more than throughput. Distributed deployments add a platform engineer.
Our developers join your repository, board, and chat workspace. Weekly sessions walk through actual conversation transcripts and per-run cost, which shows progress more clearly than a written summary. You get direct developer contact rather than an account layer.
Inside a container you own, in your own environment, with no credentials mounted and outbound access limited to an allowlist. A mutual NDA precedes repository access. Data supplied to the sandbox is scoped read-only, every execution is logged, and nothing from your engagement is reused elsewhere.
We staff for at least four hours of daily overlap with your business day across North American, UK, European, and Australian schedules. Reviews and incident response sit inside that window while build work continues outside it.
Retainers cover execution image rebuilds, dependency and security updates, framework version migrations, cost review, and incident response with agreed response times. Where you prefer internal ownership, handover includes runbooks and a knowledge transfer period at no additional charge.
Stay where the system is stable, contained, and not under active development, since a working agent system carries real value and migration consumes weeks. Migrate where you need asynchronous throughput, distributed agents, or ongoing feature work, because the earlier programming model receives no further attention and its dependency surface grows more awkward over time. We assess your case in the audit and give a direct recommendation either way rather than assuming newer is better.
Tell us what you’re building. Our team will get back to you within one business day with a clear, no-obligation plan.
